Статті в журналах: "Human body partys detecting"

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Johan, Nurul Fatiha, Yasir Mohd Mustafah, and Nahrul Khair Alang Md Rashid. "Human Body Parts Detection Using YCbCr Color Space." Applied Mechanics and Materials 393 (September 2013): 556–60. http://dx.doi.org/10.4028/www.scientific.net/amm.393.556.

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Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

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Miao, Ying, Danyang Shao, and Zhimin Yan. "Privacy-Oriented Successive Approximation Image Position Follower Processing." Complexity 2021 (June 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6853809.

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In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.

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BARANWAL, MAYANK, M. TAHIR KHAN, and CLARENCE W. DE SILVA. "ABNORMAL MOTION DETECTION IN REAL TIME USING VIDEO SURVEILLANCE AND BODY SENSORS." International Journal of Information Acquisition 08, no. 02 (June 2011): 103–16. http://dx.doi.org/10.1142/s0219878911002379.

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This paper presents a method for detecting abnormal motion in real time using a computer vision system. The method is based on the modeling of human body image, which takes into account both orientation and velocity of prominent body parts. A comparative study is made of this method with other existing algorithms based on optical flow and the use of accelerometer body sensors. From the real time experiments conducted in the present work, the developed method is found to be efficient in characterizing human motion and classifying it into basic types such as falling, sitting, and walking. The method uses a Radial Basis Function Network (RBFN) to compute the severity coefficient associated with the type of motion, based on experience. The paper evaluates the various methods and incorporates the advantages of other methods in order to develop a more reliable system for abnormal motion detection.

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4

Hong, Sungjin, and Yejin Kim. "Dynamic Pose Estimation Using Multiple RGB-D Cameras." Sensors 18, no. 11 (November 10, 2018): 3865. http://dx.doi.org/10.3390/s18113865.

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Human poses are difficult to estimate due to the complicated body structure and the self-occlusion problem. In this paper, we introduce a marker-less system for human pose estimation by detecting and tracking key body parts, namely the head, hands, and feet. Given color and depth images captured by multiple red, green, blue, and depth (RGB-D) cameras, our system constructs a graph model with segmented regions from each camera and detects the key body parts as a set of extreme points based on accumulative geodesic distances in the graph. During the search process, local detection using a supervised learning model is utilized to match local body features. A final set of extreme points is selected with a voting scheme and tracked with physical constraints from the unified data received from the multiple cameras. During the tracking process, a Kalman filter-based method is introduced to reduce positional noises and to recover from a failure of tracking extremes. Our system shows an average of 87% accuracy against the commercial system, which outperforms the previous multi-Kinects system, and can be applied to recognize a human action or to synthesize a motion sequence from a few key poses using a small set of extremes as input data.

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Ocepek, Marko, Anja Žnidar, Miha Lavrič, Dejan Škorjanc, and Inger Lise Andersen. "DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming." Agriculture 12, no. 1 (December 21, 2021): 2. http://dx.doi.org/10.3390/agriculture12010002.

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The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

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Myllylä, Teemu, Vesa Korhonen, Erkki Vihriälä, Hannu Sorvoja, Tuija Hiltunen, Osmo Tervonen, and Vesa Kiviniemi. "Human Heart Pulse Wave Responses Measured Simultaneously at Several Sensor Placements by Two MR-Compatible Fibre Optic Methods." Journal of Sensors 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/769613.

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This paper presents experimental measurements conducted using two noninvasive fibre optic methods for detecting heart pulse waves in the human body. Both methods can be used in conjunction with magnetic resonance imaging (MRI). For comparison, the paper also performs an MRI-compatible electrocardiogram (ECG) measurement. By the simultaneous use of different measurement methods, the propagation of pressure waves generated by each heart pulse can be sensed extensively in different areas of the human body and at different depths, for example, on the chest and forehead and at the fingertip. An accurate determination of a pulse wave allows calculating the pulse transit time (PTT) of a particular heart pulse in different parts of the human body. This result can then be used to estimate the pulse wave velocity of blood flow in different places. Both measurement methods are realized using magnetic resonance-compatible fibres, which makes the methods applicable to the MRI environment. One of the developed sensors is an extraordinary accelerometer sensor, while the other one is a more common sensor based on photoplethysmography. All measurements, involving several test patients, were performed both inside and outside an MRI room. Measurements inside the MRI room were conducted using a 3-Tesla strength closed MRI scanner in the Department of Diagnostic Radiology at the Oulu University Hospital.

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Mustafa, Rashed, Yang Min, and Dingju Zhu. "Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/753860.

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Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

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Gan, Lu, Aobo Geng, Ying Wu, Linjie Wang, Xingyu Fang, Lijie Xu, and Changtong Mei. "Antibacterial, Flexible, and Conductive Membrane Based on MWCNTs/Ag Coated Electro-Spun PLA Nanofibrous Scaffolds as Wearable Fabric for Body Motion Sensing." Polymers 12, no. 1 (January 5, 2020): 120. http://dx.doi.org/10.3390/polym12010120.

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In the present study, flexible and conductive nanofiber membranes were prepared by coating PLA nanofibrous scaffolds with carbon nanotubes and silver nanoparticles. The morphology and structure of the prepared membrane was characterized, as well as its mechanical properties, electrical sensing behavior during consecutive stretching-releasing cycles and human motion detecting performance. Furthermore, the antibacterial properties of the membrane was also investigated. Due to the synergistic and interconnected three-dimensional (3D) conductive networks, formed by carbon nanotubes and silver nanoparticles, the membrane exhibited repeatable and durable strain-dependent sensitivity. Further, the prepared membrane could accurately detect the motions of different body parts. Accompanied with promising antibacterial properties and washing fastness, the prepared flexible and conductive membrane provides great application potential as a wearable fabric for real-time body motion sensing.

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Adolf, Jindrich, Jaromir Dolezal, Patrik Kutilek, Jan Hejda, and Lenka Lhotska. "Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset." Applied Sciences 12, no. 2 (January 13, 2022): 799. http://dx.doi.org/10.3390/app12020799.

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In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.

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Jang, Seok-Woo, and Gye-Young Kim. "Learning-Based Detection of Harmful Data in Mobile Devices." Mobile Information Systems 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3919134.

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The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.

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van Stein, K. R., B. Strauß, and K. Brenk-Franz. "Ovulatory Shifts in Sexual Desire But Not Mate Preferences: An LH-Test-Confirmed, Longitudinal Study." Evolutionary Psychology 17, no. 2 (April 1, 2019): 147470491984811. http://dx.doi.org/10.1177/1474704919848116.

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The presented data are part of a longitudinal within-subject study designed to examine ovulatory shifts in human sexuality in a diverse German sample using validated questionnaires. The final sample consists of 78 individuals (76 female, 2 agender) who declared to be mainly or exclusively attracted to males. Questionnaires were completed anonymously online at three cycle phases. Following the gold standard, the fertile window was calculated through the reverse cycle day method and confirmed via urinary tests detecting luteinizing hormone. The questionnaire included the Sexual Desire Inventory, Dresdner Body Image Inventory, the Revised Sociosexual Orientation Inventory, and an adjective list to measure mate preferences. One hundred eighty-four questionnaires were included in the data analysis using linear mixed models. Findings support previous research reporting heightened sexual desire and an improved body image during the fertile window. No shifts were found for mate preference or sociosexual orientation, thus adding to a growing body of literature contesting parts of the ovulatory shift hypothesis.

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Kashevnik, Alexey, Walaa Othman, Igor Ryabchikov, and Nikolay Shilov. "Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis." Sensors 21, no. 11 (May 29, 2021): 3771. http://dx.doi.org/10.3390/s21113771.

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Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

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Cheng, Chung-En, Sripansuang Tangsuwanjinda, Hsin-Ming Cheng, and Po-Han Lee. "Copper Oxide Decorated Zinc Oxide Nanostructures for the Production of a Non-Enzymatic Glucose Sensor." Coatings 11, no. 8 (August 4, 2021): 936. http://dx.doi.org/10.3390/coatings11080936.

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The glucose concentration in human blood can have a worrisome impact on human health, so the distribution of blood glucose contaminants in the human body is an important indicator that can be used to monitor diabetes. Diabetes affects many parts of the human body, such as neurological impairment, erectile dysfunction, and hardening of the arteries resulting in organ loss. In this study, cyclic voltammetry (CV) was used to process the electrical properties of a solution by preparing electrodes with CuO nanoparticles modified ZnO tetrapod nanostructures deposited on fluorine-doped tin oxide glass (CuO/ZnO/FTO). The measurements were processed in glucose solutions of different concentrations purposing for developing the sensitivity of the sensor. Different immersion times in the precursor copper sulfate solution were also used for preparing the electrode and carried out for electrochemical studies to adjust the electrode capability. The modified electrode, which was immersed in copper sulfate for 30 s, was efficient in detecting glucose molecules in different concentrations at the potential of +0.6 V. The rising slope is strongly and positively correlated with the concentration of glucose. One of the significant results is the indication that glucose concentration is linearly proportional to the current value of CV. After the measurement test with the addition of interference, the sensor can still identify the glucose concentration in the solution without being affected. This result proves that the sensor has considerable potential for developing into a high-performance non-enzymatic glucose sensor.

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Kormilitsyna, V. G., V. G. Zaletaeva, S. O. Sharapchenko, R. Sh Saidgareev, M. Yu Sinyak, and N. I. Gabrielyan. "Comparison of clinical and cost effectiveness of accelerated study of sterility of intravascular catheters and drains." Medical alphabet, no. 32 (December 17, 2021): 44–47. http://dx.doi.org/10.33667/2078-5631-2021-32-44-47.

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The results of a new method for detecting the contamination of intravascular catheters and drains are presented to assess its clinical and cost-effectiveness. Catheters are one of the most widely used devices in critically ill patients. The insertion of a catheter into the central venous system is an invasive procedure that can potentially lead to life-threatening complications for the patient. Catheters are a gateway for infection as they connect the external environment to the internal parts of the human body, causing catheter-associated infections. More than 15 % of patients with an established IVC develop complications, of which the most frequent and requiring removal of the vascular catheter are infectious (5–26 %) and mechanical (up to 25 %). Risk factors for catheter-associated conditions are crucial for hospital mortality.

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Liu, Chengming, Ronghua Fu, Yinghao Li, Yufei Gao, Lei Shi, and Weiwei Li. "A Self-Attention Augmented Graph Convolutional Clustering Networks for Skeleton-Based Video Anomaly Behavior Detection." Applied Sciences 12, no. 1 (December 21, 2021): 4. http://dx.doi.org/10.3390/app12010004.

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In this paper, we propose a new method for detecting abnormal human behavior based on skeleton features using self-attention augment graph convolution. The skeleton data have been proved to be robust to the complex background, illumination changes, and dynamic camera scenes and are naturally constructed as a graph in non-Euclidean space. Particularly, the establishment of spatial temporal graph convolutional networks (ST-GCN) can effectively learn the spatio-temporal relationships of Non-Euclidean Structure Data. However, it only operates on local neighborhood nodes and thereby lacks global information. We propose a novel spatial temporal self-attention augmented graph convolutional networks (SAA-Graph) by combining improved spatial graph convolution operator with a modified transformer self-attention operator to capture both local and global information of the joints. The spatial self-attention augmented module is used to understand the intra-frame relationships between human body parts. As far as we know, we are the first group to utilize self-attention for video anomaly detection tasks by enhancing spatial temporal graph convolution. Moreover, to validate the proposed model, we performed extensive experiments on two large-scale publicly standard datasets (i.e., ShanghaiTech Campus and CUHK Avenue datasets) which reveal the state-of-art performance for our proposed approach when compared to existing skeleton-based methods and graph convolution methods.

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Ismail, Ismail, Yefriadi Y, Yuhefizar Y, Fibriyanti, and Zulka Hendri. "The Generating Super Resolution of Thermal Image based on Deep Learning." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (April 29, 2022): 289–94. http://dx.doi.org/10.29207/resti.v6i2.3934.

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The need of high resolution to thermal image is very urgent and important. The high resolution of thermal image is able to give the accurate information on heat distribution map of the objects. The accurate heat distribution maps is able to give accurate temperature information. This accurate temperature measurement is used for measuring many objects such as electric motors, engines, human body and so on. These information used for detecting the anomalies of the object in order to find the damaged parts. The anomalies are considered as damaged parts find in solar panel, agriculture field, the building, bridges and so on. As the super resolution of thermal image is very important, that the generating of them is a compulsory. Whereas, the camera for obtaining the super resolution thermal images are very rare, not available at common market. Furthermore, this kind of device is very expensive too. Therefore not all the user such farmer or technician are able to have them. In order to handle the problem, the proposed method has the purpose to generate super resolution thermal image economically and easier through deep learning method. The dataset is taken from solar panel. The results show that the proposed method is able to handle the low resolution problem of thermal images.

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Korposh, Sergiy, and Seung-Woo Lee. "A Preliminary Study for Tunable Optical Assessment of Exhaled Breath Ammonia Based on Ultrathin Tetrakis(4-sulfophenyl)porphine Nanoassembled Films." Chemosensors 9, no. 9 (September 18, 2021): 269. http://dx.doi.org/10.3390/chemosensors9090269.

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The detection of chemical substances excreted from the human body offers an attractive approach for non-invasive, early diagnostics of certain diseases. In this preliminary study, we proposed a susceptible optical sensor capable of quantitatively detecting ammonia from exhaled breath. The proposed sensor consists of nanoassembled ultrathin films composed of tetrakis(4-sulfophenyl)porphine (TSPP) and poly(diallyldimethylammonium chloride) (PDDA) deposited on quartz substrates using a layer-by-layer method. Measurement principles are based on the ammonia-induced absorbance changes at 489 (Soret band) and 702 nm (Q band), associated with the deprotonation of the J-aggregated TSPPs inside the film. Before exposure to breath, the PDDA/TSPP thin film was calibrated using known concentrations of ammonia gases with a projected detection limit of 102 ± 12 parts per billion (ppb). Calibrated sensor films were then exposed to human breath and urine samples to determine the ammonia concentration. Concentrations of exhaled ammonia are influenced significantly by the consumption of food or the amount of urea. Sensor response and maximum sensitivity, obtained from the absorbance changes induced by ammonia, were achieved by initial sensor exposure to HCl vapor. Previously reported procedures for the Helicobacter pylori (HELIC Ammonia Breath) test based on urea reaction with urease were reproduced using the proposed sensor. The observed behavior corresponded very well with the kinetics of the interactions between urea and urease, i.e., ammonia reached a maximum concentration approximately 5 min after the start of the reaction. A large-scale study involving 41 healthy volunteers in their 20s to 60s was successfully conducted to test the capabilities of the sensor to determine the concentration of exhaled ammonia. The concentration of ammonia for the healthy volunteers ranged between 0.3 and 1.5 ppm, with a mean value of ca. 520 ppb in the morning (before eating) and ca. 420 ppb in the afternoon (immediately after eating). These real-test mean values are meaningful when considered against the projected LOD.

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Kolková, Zuzana, Peter Hrabovský, Jozef Matušov, and Radovan Nosek. "Analysis of thermodynamic parameters and their influence on the thermal comfort in the working environment." MATEC Web of Conferences 168 (2018): 04002. http://dx.doi.org/10.1051/matecconf/201816804002.

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Air quality inside the building is an important measure of comfort for people living and working in it. Achieving optimal air quality requires a regulated indoor air control system, often in combination with thermal regeneration. Typical short ventilation, irregular window and door ventilation are rarely adequate. From the point of view of the thermal comfort of the environment, it is necessary to predict the development and selection of suitable systems for the thermal comfort of the environment to predict how many thermal factors will affect the population, room temperature uniformity, asymmetry of the radiation temperature, and turbulence intensity of the flowing air. People are capable of detecting heat, temperature changes can feel not only globally, but also as local heat loss on the parts of the body. Thermal comfort is given by the air temperature and radiant heat, air velocity and relative humidity in the room. The main impact of these factors on the comfort level of individuals depends on the activity and type of clothing. The bad thermal comfort is detrimental to workers' health, increasing the risk of accidents and affects adverse effects on the psyche. The assessment of impact of the working environment to human form comparing actual and optimal parameter values of the working environment.

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Klimaszewski, Jan, and Michał Władziński. "Human Body Parts Proximity Measurement Using Distributed Tactile Robotic Skin." Sensors 21, no. 6 (March 18, 2021): 2138. http://dx.doi.org/10.3390/s21062138.

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Safety in human–machine cooperation is the current challenge in robotics. Safe human–robot interaction requires the development of sensors that detect human presence in the robot’s workspace. Detection of this presence should occur before the physical collision of the robot with the human. Human to robot proximity detection should be very fast, allowing machine elements deceleration to velocities safe for human–machine collision. The paper presents a new, low-cost design of distributed robotic skin, which allows real-time measurements of the human body parts proximity. The main advantages of the proposed solution are low cost of its implementation based on comb electrodes matrix and real-time operation due to fast and simple electronic design. The main contribution is the new idea of measuring the distance to human body parts by measuring the operating frequency of a rectangular signal generator, which depends on the capacity of the open capacitor. This capacitor is formed between the comb electrodes matrix and a reference plate located next to the matrix. The capacitance of the open capacitor changes if a human body part is in vicinity. The application of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human–machine interfaces and increased safety of human–machine cooperation. The proposed construction can help to meet the increasing requirements for cooperative robots.

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Hong, Sungjin, and Myounggyu Kim. "A Framework for Human Body Parts Detection in RGB-D Image." Journal of Korea Multimedia Society 19, no. 12 (December 31, 2016): 1927–35. http://dx.doi.org/10.9717/kmms.2016.19.12.1927.

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Samad, Rosdiyana, Law Wen Yan, Mahfuzah Mustafa, Nor Rul Hasma Abdullah, and Dwi Pebrianti. "Multiple Human Body Postures Detection using Kinect." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 528. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp528-536.

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<span lang="EN-US">This paper presents a method to detect multiple human body postures using Kinect sensor. In this study, a combination of shape features and body joint points are used as input features. The Kinect sensor which used infrared camera to produce a depth image is suitable to be used in an environment that has varying lighting conditions. The method for human detection is done by processing the depth image and joint data (skeleton) which able to overcome several problems such as cluttered background, various articulated poses, and change in color and illumination. Then, the body joint coordinates found on the object are used to calculate the body proportion ratio. In the experiment, the average body proportions from three body parts are obtained to verify the suitableness of golden ratio usage in this work. Finally, the measured body proportion is compared with Golden Ratio to determine whether the found object is a real human body or not. This method is tested for various scenarios, where true positive human detection is high for various postures. This method able to detect a human body in low lighting and dark room. The average body proportions obtained from the experiment show that the value is close to the golden ratio value.</span>

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Yang, Guiyi, Zhengyou Wang, Shanna Zhuang, and Hui Wang. "PFF-CB: Multiscale Occlusion Pedestrian Detection Method Based on PFF and CBAM." Computational Intelligence and Neuroscience 2022 (April 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/3798060.

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Occlusion pedestrian detection is an important and difficult task in pedestrian detection. At present, the main method to deal with occlusion pedestrian detection usually adopts pedestrian parts or human body relationship methods. However, in the scene of crowd occlusion or severe pedestrian occlusion, only small parts of the body can be used for detection. Pedestrian parts or human body relationship methods cannot effectively address these issues. In view of the above problems, this paper abandoned the occlusion processing method of pedestrian parts or human body relationship. Considering that it is difficult to establish the relationship between parts and key points. The scale of visible parts of the occlusion pedestrian is small, and the scale of no occlusion pedestrian and occlusion pedestrian in the same picture is different. A multiscale feature attention fusion network named parallel feature fusion with CBAM (PFF-CB) is proposed for occlusion pedestrian detection. Feature information of different scales can be integrated effectively in the PFF-CB module. PFF-CB module uses a convolutional block attention module (CBAM) to enhance the important feature information in space and channel. A parallel feature fusion module based on FPN is used to enhance key features. The performance of the proposed module was tested on two common data sets of occlusion pedestrians with different occlusion types. The results show that the PFF-CB module makes a good performance in occlusion pedestrian detection tasks.

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Bhaskar, Harish, Lyudmila Mihaylova, and Simon Maskell. "Articulated human body parts detection based on cluster background subtraction and foreground matching." Neurocomputing 100 (January 2013): 58–73. http://dx.doi.org/10.1016/j.neucom.2011.12.039.

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Kwak, Nae-Joung, and Teuk-Seob Song. "Automatic Detecting of Joint of Human Body and Mapping of Human Body using Humanoid Modeling." Journal of the Korean Institute of Information and Communication Engineering 15, no. 4 (April 30, 2011): 851–59. http://dx.doi.org/10.6109/jkiice.2011.15.4.851.

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Ghadi, Yazeed, Israr Akhter, Mohammed Alarfaj, Ahmad Jalal, and Kibum Kim. "Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning." PeerJ Computer Science 7 (November 19, 2021): e764. http://dx.doi.org/10.7717/peerj-cs.764.

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The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.

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Xiong Zonglong, 熊宗龙, 杨坤涛 Yang Kuntao, and 张南洋生 Zhang Nanyangsheng. "Detecting Model of Infrared Radiation of Human Body Indoors." Acta Optica Sinica 29, no. 12 (2009): 3379–84. http://dx.doi.org/10.3788/aos20092912.3379.

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Mao, Aihua, Yuan Liang, Jianbo Jiao, Yongtuo Liu, and Shengfeng He. "Mask-Guided Deformation Adaptive Network for Human Parsing." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (January 31, 2022): 1–20. http://dx.doi.org/10.1145/3467889.

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Due to the challenges of densely compacted body parts, nonrigid clothing items, and severe overlap in crowd scenes, human parsing needs to focus more on multilevel feature representations compared to general scene parsing tasks. Based on this observation, we propose to introduce the auxiliary task of human mask and edge detection to facilitate human parsing. Different from human parsing, which exploits the discriminative features of each category, human mask and edge detection emphasizes the boundaries of semantic parsing regions and the difference between foreground humans and background clutter, which benefits the parsing predictions of crowd scenes and small human parts. Specifically, we extract human mask and edge labels from the human parsing annotations and train a shared encoder with three independent decoders for the three mutually beneficial tasks. Furthermore, the decoder feature maps of the human mask prediction branch are further exploited as attention maps, indicating human regions to facilitate the decoding process of human parsing and human edge detection. In addition to these auxiliary tasks, we further alleviate the problem of deformed clothing items under various human poses by tracking the deformation patterns with the deformable convolution. Extensive experiments show that the proposed method can achieve superior performance against state-of-the-art methods on both single and multiple human parsing datasets. Codes and trained models are available https://github.com/ViktorLiang/MGDAN .

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Reddy Gurunatha Swamy, P., and B. Ananth Reddy. "Human Pose Estimation in Images and Videos." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i3.27.17640.

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Estimation of human poses is an interesting and challenging topic in the field of Computer vision. It includes some un-noticed challenges like background effect, the color of the dress, skin tones and many other unpredictable challenges. This is a workable concept because it can be used in sign language recognition, correlating various pose styles from different parts of the world and in medical applications. A deep structure which can represent a man’s body in different models will help in improved recognition of body parts and the spatial correlation between them. For hand detection, features based on hand shape and representation of geometrical details are derived with the help of hand contour. An adaptive and unsupervised approach based on Voronoi region is primarily used for the color image segmentation problem. This process includes identification of key points of the body, which may include body joints and parts. The identification parts will be tough due to small joints and occlusions. Identification of Image features is described in this paper with the help of Box Model Based Estimation, Speed up robust features and finally with Optical flow tracking algorithm. In Optical flow tracking algorithm, we have used Horn-Schunk algorithm to determine featural changes in the images.

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29

Kwak, Nae-Joung, and Teuk-Seob Song. "Automatic Detecting and Tracking Algorithm of Joint of Human Body using Human Ratio." Journal of the Korea Contents Association 11, no. 4 (April 28, 2011): 215–24. http://dx.doi.org/10.5392/jkca.2011.11.4.215.

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30

Wulandari, Titis, Randi Eka Putra, and Tri Wera Agrita. "ESCHERICHIA COLI BACTERIA DETECTION IN DRINKING WATER DEPOTS BUNGO DISTRICT." BIOEDUKASI 19, no. 2 (October 31, 2021): 96. http://dx.doi.org/10.19184/bioedu.v19i2.25405.

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Water is a very important component in life, such as the use of water as drinking water, all living things, both humans, animals, and plants, need water. Part of the human body consists of fluids, so water helps the body to work to carry nutrients from the stomach to all parts of the body. So that a hygienic water source is needed, through a processing process first until the water meets health requirements. The purpose of this study was to detect the presence of Escherichia coli bacteria in drinking water depots in two sub-districts in Bungo Regency, namely (Rimbo Tengah, Pasar Muara Bungo). The test was carried out at the Fish Quarantine Station and Quality Control Laboratory of Class 1 Jambi. Direct sampling with sterile bottles. The sample testing method uses the Most Probable Number (MPN) test with SNI ISO 9308-1:2014. Direct sampling with sterile bottles. The sample testing method uses the Most Probable Number (MPN) test with SNI ISO 9308-1:2014.

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Kapilevich, Boris, and Moshe Einat. "Detecting Hidden Objects on Human Body Using Active Millimeter Wave Sensor." IEEE Sensors Journal 10, no. 11 (November 2010): 1746–52. http://dx.doi.org/10.1109/jsen.2010.2049350.

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Nakashima, Yuuki, Joo Kooi Tan, Seiji Ishikawa, and Takashi Morie. "On detecting a human and its body direction from a video." Artificial Life and Robotics 15, no. 4 (December 2010): 455–58. http://dx.doi.org/10.1007/s10015-010-0841-4.

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Senjaya, Benny, Alexander A. S. Gunawan, and Jerry Pratama Hakim. "Pendeteksian Bagian Tubuh Manusia untuk Filter Pornografi dengan Metode Viola-Jones." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (June 1, 2012): 482. http://dx.doi.org/10.21512/comtech.v3i1.2447.

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Information Technology does help people to get information promptly anytime and anywhere. Unfortunately, the information gathered from the Internet does not always come out positive. Some information can be destructive, such as porn images. To mitigate this problem, the study aims to create a desktop application that could detect parts of human body which can be expanded in the future to become an image filter application for pornography. The detection methodology in this study is Viola-Jones method which provides a complete framework for extracting and recognizing image features. A combination of Viola-Jones method with Haar-like features, integral image, boosting algorithm, and cascade classifier provide a robust detector for the application. First, several parts of the human body are chosen to be detected as the data training using the Viola-Jones method. Then, another set of images (similar body parts but different images) are run through the application to be recognized. The result shows 86.25% of successful detection. The failures are identified and show that the inputted data are completely different with the data training.

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Gao, Hua, Shengyong Chen, and Zhaosheng Zhang. "Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification." Applied Sciences 9, no. 6 (March 25, 2019): 1239. http://dx.doi.org/10.3390/app9061239.

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Person re-identification is a typical computer vision problem which aims at matching pedestrians across disjoint camera views. It is challenging due to the misalignment of body parts caused by pose variations, background clutter, detection errors, camera point of view variation, different accessories and occlusion. In this paper, we propose a person re-identification network which fuses global and local features, to deal with part misalignment problem. The network is a four-branch convolutional neural network (CNN) which learns global person appearance and local features of three human body parts respectively. Local patches, including the head, torso and lower body, are segmented by using a U_Net semantic segmentation CNN architecture. All four feature maps are then concatenated and fused to represent a person image. We propose a DropParts method to solve the parts missing problem, with which the local features are weighed according to the number of parts found by semantic segmentation. Since three body parts are well aligned, the approach significantly improves person re-identification. Experiments on the standard benchmark datasets, such as Market1501, CUHK03 and DukeMTMC-reID datasets, show the effectiveness of our proposed pipeline.

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35

Jariwala, Krishna B., and Prof Jaimeel Shah. "Survey of Detecting Heartbeats, Temperature and ECG of Human Body using IOT." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2457–61. http://dx.doi.org/10.31142/ijtsrd17153.

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36

Nadeem, Amir, Ahmad Jalal, and Kibum Kim. "Automatic human posture estimation for sport activity recognition with robust body parts detection and entropy markov model." Multimedia Tools and Applications 80, no. 14 (March 16, 2021): 21465–98. http://dx.doi.org/10.1007/s11042-021-10687-5.

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37

Naghdi, Sharareh, and Kyle O’Keefe. "Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence." Sensors 20, no. 5 (February 29, 2020): 1350. http://dx.doi.org/10.3390/s20051350.

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One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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38

Sefidgari, Bahram Lavi. "Feedback Method Based on Image Processing for Detecting Human Body via Flying Robot." International Journal of Artificial Intelligence & Applications 4, no. 6 (November 30, 2013): 35–44. http://dx.doi.org/10.5121/ijaia.2013.4604.

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39

Kumar, N., and N. Sukavanam. "An improved CNN framework for detecting and tracking human body in unconstraint environment." Knowledge-Based Systems 193 (April 2020): 105198. http://dx.doi.org/10.1016/j.knosys.2019.105198.

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40

Koźbiał, Michał, Łukasz Markiewicz, and Robert Sitnik. "Algorithm for Detecting Characteristic Points on a Three-Dimensional, Whole-Body Human Scan." Applied Sciences 10, no. 4 (February 16, 2020): 1342. http://dx.doi.org/10.3390/app10041342.

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Anthropometric landmarks obtained from three-dimensional (3D) body scans are widely used in medicine, civil engineering, and virtual reality. For all those fields, an acquisition of certain and accurate landmark positions is crucial for obtaining satisfying results. Manual marking is time-consuming and is affected by the subjectivity of the human operator. Therefore, an automatic approach has become increasingly popular. This paper provides a short survey of different attempts for automatic landmark localization, from which one machine learning-based method was further analyzed and extended in the case of input data preparation for a convolutional neural network (CNN). A novel method of data processing is presented which utilize a mid-surface projection followed by further unwrapping. The article emphasizes its significance and the way it affects the outcome of a deep neural network. The workflow and the detailed description of algorithms used are included in this paper. To validate the method, it was compared with the orthogonal projection used for the state-of-the-art approach. Datasets consisting of 200 specimens, acquired using both methods, were used for convolutional neural networks training and 20 for validation. In this paper, we used YOLO v.3 architecture for detection and ResNet-152 for classification. For each approach, localizations of 22 normalized body landmarks for 10 male and 10 female subjects of different ages and various postures were obtained. To compare the accuracy of approaches, errors and their distribution were measured for each characteristic point. Experiments confirmed that the mid-surface projections resulted, on average, in a 14% accuracy improvement and up to 15% enhancement of resistance on errors related to scan imperfections.

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Nadeem, Amir, Ahmad Jalal, and Kibum Kim. "Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness." Symmetry 12, no. 11 (October 24, 2020): 1766. http://dx.doi.org/10.3390/sym12111766.

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Recent developments in sensor technologies enable physical activity recognition (PAR) as an essential tool for smart health monitoring and for fitness exercises. For efficient PAR, model representation and training are significant factors contributing to the ultimate success of recognition systems because model representation and accurate detection of body parts and physical activities cannot be distinguished if the system is not well trained. This paper provides a unified framework that explores multidimensional features with the help of a fusion of body part models and quadratic discriminant analysis which uses these features for markerless human pose estimation. Multilevel features are extracted as displacement parameters to work as spatiotemporal properties. These properties represent the respective positions of the body parts with respect to time. Finally, these features are processed by a maximum entropy Markov model as a recognition engine based on transition and emission probability values. Experimental results demonstrate that the proposed model produces more accurate results compared to the state-of-the-art methods for both body part detection and for physical activity recognition. The accuracy of the proposed method for body part detection is 90.91% on a University of Central Florida’s (UCF) sports action dataset and, for activity recognition on a UCF YouTube action dataset and an IM-DailyRGBEvents dataset, accuracy is 89.09% and 88.26% respectively.

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42

Abdullah, Ahmed Saadi, Majid Hamid Ali, and Mohammed Waleed. "Distributed Prewitt Edge Detection System Using Lightness of Ycbcr Color Space." Webology 19, no. 1 (January 20, 2022): 1460–73. http://dx.doi.org/10.14704/web/v19i1/web19097.

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Recently, image processing has been widely used in the medical field. This field helps to increase the accuracy of medical diagnosis, which helps in the early detection of diseases and increases the accuracy of prescribing treatment. Where it is noted that most medical devices such as sonar that are used to take pictures of body parts may give blurred images, which leads to the need for digital image processing techniques to increase the clarity of these images, which gives an accurate description of the examined part of the body. Speed is also one of the most important criteria for measuring the quality of any system, especially in the critical matters, including the medical field, where speed and accuracy of diagnosis are very important for immediate decision-making. To ensure the best speed and the most accurate result, it is better to distribute this process and use more than one processing unit for digital image processing. In this paper, a system was presented that detects the edges of medical images by relying on edge detection processing technology and taking advantage of the field of distributed systems to obtain results at high speed and accuracy. Matlab 2015 environment was used to simulate the system, and the results showed high accuracy in edge detection and high speed to obtain the results.

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43

Patil, Santosh, N. Ramakrishnaiah, and S. Laxman Kumar. "Enhanced approach for face detection and identifying human body proportionality using v-jones algorithm." International Journal of Engineering & Technology 7, no. 4 (September 17, 2018): 2374. http://dx.doi.org/10.14419/ijet.v7i4.14734.

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Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos. Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos. In this proposed work we use Viola jones method for detecting moving human object, next using same method we identify the Human anatomy body proportion to detect the whole human body. The final function is the skin color threshold using the HIS and YCbCr. The proposed method yields high accuracy, we conducted experimental analysis on different videos, achieved high accuracy in detecting human object moment. Several future enhancements can be made to the system. The detection and tracking of multiple people can be extended to real-time live video. Apart from the detection and tracking, process of recognition can also be done.

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44

Gao, Da Peng, Qing Xing Zhu, Chao Rong Li, and Ting Yuan Li. "A Novel Image Segmentation Enhance Technology for Motion Human Body." Advanced Materials Research 875-877 (February 2014): 2006–13. http://dx.doi.org/10.4028/www.scientific.net/amr.875-877.2006.

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In this paper, a novel technology, which detects and forecasts human's abnormal behavior, is proposed. Taking an abnormal behavior, the robbery, as an example, the accuracy of the technology is demonstrated successfully. There are 3 innovations in this paper. A) A robbery incident is divided into 5 phases occurred orderly. By its order, a robbery is detected. B) By human body's motion character, the human body's speeds are measured in different areas and at different times. It improves the accuracy rate of a classifier. C) The technology can forecast an abnormal human behavior in a short time advance. Experiment shows that the technology detecting and forecasting accuracy are high enough to be used in practice.

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Hanganu, Bianca, Andreea Alexandra Velnic, Valentin Petre Ciudin, Dragos Crauciuc, Camelia Liana Buhas, Irina Smaranda Manoilescu, Laura Gheuca Solovastru, and Beatrice Gabriela Ioan. "The Study of Natural Saponification Processes in Preservation of Human Corpses." Revista de Chimie 68, no. 12 (January 15, 2018): 2948–51. http://dx.doi.org/10.37358/rc.17.12.6013.

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The natural course of evolution of the human body after death includes autolysis, putrefaction and skeletonization. Under specific environmental conditions, the body may bypass this natural path, leading to preservation, such as saponification (adipocere), lignification, mummification or refrigeration, comprising the entire body or being limited only to some parts of it. All these preservation processes have a chemical substrate, and the identification of their components may be useful in many forensic circumstances, such as clandestine activity and identification of victims in mass disasters, when pentanoic, butanoic, hexanoic acid, butanoic acid-butyl ester, hexanoic acid-ethyl ester, indole, dimethyl disulphide may be used to train human remains detection dogs. The authors present the case of a 73 years old woman who was found dead in a sewage collection basin 4 months after her disappearance in July. The autopsy revealed a mixture of cadaveric processes, some parts being disintegrated, with putrefaction and skeletonization, while other parts were preserved by saponification: areas of adipocere were found on the neck, thorax and the anterior part of the abdomen. In some instances the saponification of the corpses makes identification possible and preserves violence marks, but the combination with putrefaction burdens these goals. The identification in this case was possible using the teeth formula, keeping in mind that tooth and bones are the most resistant to putrefaction. Even though saponification makes difficult the estimation of postmortem interval, investigations of its chemical composition may be useful in this direction, as the epicoprostanol-cholesterol ratio proved to increase with the increasing of postmortem interval.

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Wang, Haiyun, and Shujun Hu. "Gymnastics Movement Signs Based on Network Communication and Body Contour Feature Extraction." Mobile Information Systems 2021 (October 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/8336367.

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With the rapid development of computer vision technology, human action recognition technology has occupied an important position in this field. The basic human action recognition system is mainly composed of three parts: moving target detection, feature extraction, and human action recognition. In order to understand the action signs of gymnastics, this article uses network communication and contour feature extraction to extract different morphological features during gymnastics. Then, the finite difference algorithm of edge curvature is used to classify different gymnastic actions and analyze and discuss the Gaussian background. A modular method, an improved hybrid Gaussian modeling method, is proposed, which adaptively selects the number of Gaussian distributions. The research results show that, compared with traditional contour extraction, the resolution of gymnastic motion features extracted through network communication and body contour features is clearer, and the increase rate is more than 30%. Moreover, the method proposed in this paper removes noise in the image extraction process, the effect is good, and the athlete’s action marks are very clear, which can achieve the research goal.

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Gochoo, Munkhjargal, Israr Akhter, Ahmad Jalal, and Kibum Kim. "Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network." Remote Sensing 13, no. 5 (February 28, 2021): 912. http://dx.doi.org/10.3390/rs13050912.

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Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.

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Gajic, Dusan, Gorana Gojic, Dinu Dragan, and Veljko Petrovic. "Comparative evaluation of keypoint detectors for 3d digital avatar reconstruction." Facta universitatis - series: Electronics and Energetics 33, no. 3 (2020): 379–94. http://dx.doi.org/10.2298/fuee2003379g.

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Three-dimensional personalized human avatars have been successfully utilized in shopping, entertainment, education, and health applications. However, it is still a challenging task to obtain both a complete and highly detailed avatar automatically. One approach is to use general-purpose, photogrammetry-based algorithms on a series of overlapping images of the person. We argue that the quality of avatar reconstruction can be increased by modifying parts of the photogrammetry-based algorithm pipeline to be more specifically tailored to the human body shape. In this context, we perform an extensive, standalone evaluation of eleven algorithms for keypoint detection, which is the first phase of the photogrammetry-based reconstruction pipeline. We include well established, patented Distinctive image features from scale-invariant keypoints (SIFT) and Speeded up robust features (SURF) detection algorithms as a baseline since they are widely incorporated into photogrammetry-based software. All experiments are conducted on a dataset of 378 images of human body captured in a controlled, multi-view stereo setup. Our findings are that binary detectors highly outperform commonly used SIFT-like detectors in the avatar reconstruction task, both in terms of detection speed and in number of detected keypoints.

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Olmeda, Daniel, Arturo de la Escalera, and José María Armingol. "Far infrared pedestrian detection and tracking for night driving." Robotica 29, no. 4 (July 29, 2010): 495–505. http://dx.doi.org/10.1017/s0263574710000299.

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SUMMARYThis paper presents a module for pedestrian detection from a moving vehicle in low-light conditions. The algorithm make use of a single far infrared camera based on a microbolometer. Images of the area ahead of the vehicle are analyzed to determine if any pedestrian might be in its trajectory. Detection is achieved by searching for distributions of temperatures in the scene similar to that of the human body. Those areas with an appropriate temperature, size, and position in the image are classified, by means of a correlation between them and some probabilistic models, which represents the average temperature of the different parts of the human body. Finally, those pedestrians found are tracked in a subsequent step, using an unscented Kalman filter. This final stage of the algorithm enables the algorithm to predict the trajectory of the pedestrian, in a way that does not depend on the movement of the camera. The aim of this system is to warn the vehicle's driver and reduce the reaction time in case an emergency break is necessary.

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Qu, Zhi Yi, Ya Xin Jin, and Jie Feng. "Fast Human Detection Using Dynamic Contour and Histograms of Oriented Gradients." Applied Mechanics and Materials 347-350 (August 2013): 3600–3603. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3600.

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Human detection is a challenging problem, owing to variations in pose, body shape, appearance, clothing, illumination, and background clutter, in addition, the cameras or backgrounds make it even harder. But even so, it has many potential applications including net-meeting, security, human-computer interaction, gaming, and even health-care. Various new approaches have been proposed to solve this problem. We have studied and implemented a method by using dynamic contour [ and Histograms of Oriented Gradients [ to detecting human body fast and accurately in static images.

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