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Статті в журналах з теми "Robust Human Detection"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

GUAN, F., L. Y. LI, S. S. GE, and A. P. LOH. "ROBUST HUMAN DETECTION AND IDENTIFICATION BY USING STEREO AND THERMAL IMAGES IN HUMAN ROBOT INTERACTION." International Journal of Information Acquisition 04, no. 02 (June 2007): 161–83. http://dx.doi.org/10.1142/s0219878907001241.

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Анотація:

In this paper, robust human detection is investigated by fusing the stereo and infrared thermal images for effective interaction between humans and socially interactive robots. A scale-adaptive filter is first designed for the stereo vision system to detect human candidates. To eliminate the difficulty of the vision system in distinguishing human beings from human-like objects, the infrared thermal image is used to solve the ambiguity and reduce the illumination effect. Experimental results show that the fusion of these two types of images gives an improved vision system for robust human detection and identification, which is the most important and essential component of human robot interaction.

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2

Iwata, Kenji, Yutaka Satoh, Ikushi Yoda, and Katsuhiko Sakaue. "Hybrid Camera Surveillance System Using Robust Human Detection." IEEJ Transactions on Electronics, Information and Systems 127, no. 6 (2007): 837–43. http://dx.doi.org/10.1541/ieejeiss.127.837.

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3

Al-Hazaimeh, Obaida M., Malek Al-Nawashi, and Mohamad Saraee. "Geometrical-based approach for robust human image detection." Multimedia Tools and Applications 78, no. 6 (August 4, 2018): 7029–53. http://dx.doi.org/10.1007/s11042-018-6401-y.

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4

Chowdhury, Mozammel, Junbin Gao, and Rafiqul Islam. "Robust human detection and localization in security applications." Concurrency and Computation: Practice and Experience 29, no. 23 (October 22, 2016): e3977. http://dx.doi.org/10.1002/cpe.3977.

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5

Iwata, Kenji, Yutaka Satoh, Ikushi Yoda, and Katsuhiko Sakaue. "Hybrid camera surveillance system using robust human detection." Electronics and Communications in Japan 91, no. 11 (November 2008): 11–18. http://dx.doi.org/10.1002/ecj.10006.

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6

Störring, Moritz, Hans J. Andersen, and Erik Granum. "A multispectral approach to robust human skin detection." Conference on Colour in Graphics, Imaging, and Vision 2, no. 1 (January 1, 2004): 110–15. http://dx.doi.org/10.2352/cgiv.2004.2.1.art00024.

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7

WooJang, Seok, and Siwoo Byun. "Facial region detection robust to changing backgrounds." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 25. http://dx.doi.org/10.14419/ijet.v7i2.12.11028.

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Анотація:

Background/Objectives: These days, many studies have actively been conducted on intelligent robots capable of providing human friendly service. To make natural interaction between humans and robots, it is required to develop the mobile robot-based technology of detecting human facial regions robustly in dynamically changing real backgrounds.Methods/Statistical analysis: This paper proposes a method for detecting facial regions adaptively through the mobile robot-based monitoring of backgrounds in a dynamic real environment. In the proposed method, a camera-object distance and a color change in object background are monitored, and thereby the skin color extraction algorithm most suitable for the measured distance and color is applied. In the face detection step, if the searched range is valid, the most suitable skin color detection method is selected so as to detect facial regions.Findings: To sum up the experimental results, algorithms have a difference in performance depending on a distance and a background color. Overall, the algorithms using neural network showed stable results. The algorithm using Kismet had a good perception rate for the ground truth part of an original image, and a skin color detection rate was greatly influenced by pink and yellow background colors similar to a skin tone, and consequently an incorrect perception rate of background was considerably high. With regard to each algorithm performance depending on a distance, the closer a distance with an object was to 320cm, the more an incorrect perception rate of a background sharply increased. To analyze the performance of each skin color detection algorithm applied to face detection, we examined how much a skin color of an original image was detected by each algorithm. For a skin color detection rate, after the ground truth for the skin of an original image, the number of pixels of the skin color detected by each algorithm was calculated. In this case, the ground truth means a range of the skin color of an original image to detect.Improvements/Applications: We expect that the proposed approach of detecting facial regionsin a dynamic real environment will be used in a variety of application areas related to computer vision and image processing.

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8

Zhong, Xubin, Changxing Ding, Xian Qu, and Dacheng Tao. "Polysemy Deciphering Network for Robust Human–Object Interaction Detection." International Journal of Computer Vision 129, no. 6 (April 19, 2021): 1910–29. http://dx.doi.org/10.1007/s11263-021-01458-8.

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9

CHO, SANG-HO, TAEWAN KIM, and DAIJIN KIM. "POSE ROBUST HUMAN DETECTION IN DEPTH IMAGES USING MULTIPLY-ORIENTED 2D ELLIPTICAL FILTERS." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (August 2010): 691–717. http://dx.doi.org/10.1142/s0218001410008135.

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This paper proposes a pose robust human detection and identification method for sequences of stereo images using multiply-oriented 2D elliptical filters (MO2DEFs), which can detect and identify humans regardless of scale and pose. Four 2D elliptical filters with specific orientations are applied to a 2D spatial-depth histogram, and threshold values are used to detect humans. The human pose is then determined by finding the filter whose convolution result was maximal. Candidates are verified by either detecting the face or matching head-shoulder shapes. Human identification employs the human detection method for a sequence of input stereo images and identifies them as a registered human or a new human using the Bhattacharyya distance of the color histogram. Experimental results show that (1) the accuracy of pose angle estimation is about 88%, (2) human detection using the proposed method outperforms that of using the existing Object Oriented Scale Adaptive Filter (OOSAF) by 15–20%, especially in the case of posed humans, and (3) the human identification method has a nearly perfect accuracy.

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10

SRISUK, SANUN, WERASAK KURUTACH, and KONGSAK LIMPITIKEAT. "A NOVEL APPROACH FOR ROBUST, FAST AND ACCURATE FACE DETECTION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, no. 06 (December 2001): 769–79. http://dx.doi.org/10.1142/s0218488501001228.

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In this paper, we propose a novel approach for detecting human faces in a complex background scene. This method is robust and based on our enhanced hausdorff distance. A major aim of this research is to achieve a highly efficient method of face detection that can be used in any real time applications. In addition, our approach produces a very reliable and accurate result. The whole algorithm is composed of three main modules: robust skin detection using Fuzzy HSCC, face similarity measure using RAMHD, and facial feature detection using SVM. Moreover, a technique of automatically updating the size of an elliptical model is also introduced. The results will be shown with real images.

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11

SAMAL, ASHOK, and PRASANA A. IYENGAR. "HUMAN FACE DETECTION USING SILHOUETTES." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 1995): 845–67. http://dx.doi.org/10.1142/s0218001495000353.

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Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast.

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12

Serrano-Cuerda, Juan, Antonio Fernández-Caballero, and María Teresa López. "Robust human detection through fusion of color and infrared video." ELCVIA Electronic Letters on Computer Vision and Image Analysis 13, no. 2 (June 7, 2014): 17. http://dx.doi.org/10.5565/rev/elcvia.604.

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13

Ghayoor, Ali, Jatin G. Vaidya, and Hans J. Johnson. "Robust automated constellation-based landmark detection in human brain imaging." NeuroImage 170 (April 2018): 471–81. http://dx.doi.org/10.1016/j.neuroimage.2017.04.012.

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14

Ragb, Hussin, and Vijayan Asari. "Multifeature fusion for robust human detection in thermal infrared imagery." Optical Engineering 58, no. 04 (April 9, 2019): 1. http://dx.doi.org/10.1117/1.oe.58.4.043101.

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15

MORITA, Miho, Hiroshi MIZOGUCHI, Hiroshi TAKEMURA, and Ming DING. "2P2-L01 Robust Human detection method combined with HLAC and HOG(Digital Human)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2P2—L01_1—_2P2—L01_4. http://dx.doi.org/10.1299/jsmermd.2011._2p2-l01_1.

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16

CHANG, MING-SHAUNG, and JUNG-HUA CHOU. "A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 06 (September 2010): 847–66. http://dx.doi.org/10.1142/s0218001410008214.

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Анотація:

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

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17

Orita, Yasuaki, and Takanori Fukao. "Robust Human Tracking of a Crawler Robot." Journal of Robotics and Mechatronics 31, no. 2 (April 20, 2019): 194–202. http://dx.doi.org/10.20965/jrm.2019.p0194.

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Carrying out firefighting activities at disaster sites is extremely difficult. Therefore, robots that support and enhance these operations are required. In this paper, a crawler robot that tracks the moving path of a firefighter is proposed. It is commonly believed that trained firefighters select the best route; thus, it was assumed that this route is the easiest for the crawler robot as well. Using two 3D light detection and ranging sensors, once the firefighter’s coordinates are detected, the coordinates are combined with 3D simultaneous localization and mapping results, then a target path is generated. The crawler robot follows the path using inverse optimal tracking control. The controller has a stability margin that guarantees robustness, which is an ideal property for disaster response robots used in severe conditions. The results of several experiments show that the proposed system is effective and practical for the crawler robot.

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18

KONWAR, LAKHYADEEP, ANJAN KUMAR TALUKDAR, and KANDARPA KUMAR SARMA. "Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 17 (August 6, 2021): 93–98. http://dx.doi.org/10.37394/232014.2021.17.13.

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Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human

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19

Zhao, Jijun, Lishuang Liu, Zhongcheng Wei, Chunhua Zhang, Wei Wang, and Yongjian Fan. "R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi." Sensors 19, no. 6 (March 22, 2019): 1421. http://dx.doi.org/10.3390/s19061421.

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As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.

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20

Rougier, Caroline, Jean Meunier, Alain St-Arnaud, and Jacqueline Rousseau. "Robust Video Surveillance for Fall Detection Based on Human Shape Deformation." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 5 (May 2011): 611–22. http://dx.doi.org/10.1109/tcsvt.2011.2129370.

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21

Mojarad, Roghayeh, Ferhat Attal, Abdelghani Chibani, and Yacine Amirat. "Automatic Classification Error Detection and Correction for Robust Human Activity Recognition." IEEE Robotics and Automation Letters 5, no. 2 (April 2020): 2208–15. http://dx.doi.org/10.1109/lra.2020.2970667.

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22

SONMEZOCAK, T. "Robust Human Detection Using Histogram Oriented Gradient and Aggregate Channel Features." Advances in Electrical and Computer Engineering 23, no. 2 (2023): 93–100. http://dx.doi.org/10.4316/aece.2023.02011.

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23

Alshaibani, Abdullah, and Alexander J. Quinn. "Pterodactyl: Two-Step Redaction of Images for Robust Face Deidentification." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 9 (October 4, 2021): 27–34. http://dx.doi.org/10.1609/hcomp.v9i1.18937.

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Redacting faces in images is trivial when the number of faces is small and the annotator is trusted. For large batches, automated face detection has been the only currently viable solution, yet even the best ML-based solutions have error rates that would be unacceptable for sensitive applications. Crowd-based face detection/redaction systems exist, yet the process and the cost make them not feasible. We present Pterodactyl, a system for detecting (and redacting) faces at scale. It uses the AdaptiveFocus filter, which splits the image into smaller regions and uses machine learning to select a median filter for each region to hide the facial identities in the image while simultaneously allowing those faces to be detectable by crowd workers. The filter uses a convolutional neural network trained on images associated with the median filter level that allows detection and prevents identification. This filter allows Pterodactyl to achieve human-level detection with just 14% crowd labor as another recent crowd-based face detection/redaction system (IntoFocus). Our evaluation found that the redaction accuracy was higher than a commercial machine-based application and on par with IntoFocus while requiring 86% less crowd work (number of comparable tasks).

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24

Shang, Wei, and Maryhelen Stevenson. "Detection of speech playback attacks using robust harmonic trajectories." Computer Speech & Language 65 (January 2021): 101133. http://dx.doi.org/10.1016/j.csl.2020.101133.

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25

Mehta, Bhaskar, and Wolfgang Nejdl. "Unsupervised strategies for shilling detection and robust collaborative filtering." User Modeling and User-Adapted Interaction 19, no. 1-2 (July 18, 2008): 65–97. http://dx.doi.org/10.1007/s11257-008-9050-4.

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26

Intan, Indo, Nurdin Nurdin, and Fitriaty Pangerang. "Facial recognition using multi edge detection and distance measure." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (September 1, 2023): 1330. http://dx.doi.org/10.11591/ijai.v12.i3.pp1330-1342.

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Face recognition provides broad access to several public devices, so it is essential in the midst of today's technology boom. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.

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27

Hu, Yan. "Reliability Analysis of Multi-Objective Spatio-Temporal Segmentation of Human Motion in Video Sequences." International Journal of Distributed Systems and Technologies 12, no. 1 (January 2021): 16–29. http://dx.doi.org/10.4018/ijdst.2021010102.

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In view of the problem of uneven distribution of edge contour of multi-target human motion image in video sequence, which leads to the decline of target detection ability, an algorithm of multi-target spatial-temporal segmentation of human motion in video sequence based on edge contour feature detection and block fusion is proposed. Firstly, a multi-target spatial-temporal detection model of human motion in video sequence was constructed, extracting video image frame sequence, using discrete frame fusion method to segment and fuse moving target image, matching moving multi-target in video sequence, secondly segmenting motion features in moving target image, combining with SURF algorithm (speeded up robust features, accelerated robust features) to detect and extract human motion objects in video sequence. The experimental results show that the gray histogram of human motion multi-target space-time segmentation is close to the original image histogram, and the detection and recognition ability of human motion target is improved.

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28

Zhao, Xin-Chao, Jia-Zheng Yuan, Hong-Zhe Liu, and Jian-She Zhou. "Improved AdaBoost Algorithm for Robust Real-Time Multi-face Detection." Journal of Software 12, no. 1 (January 2017): 53–61. http://dx.doi.org/10.17706/jsw.12.1.53-61.

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29

Lv, Jiguang, Dapeng Man, Wu Yang, Liangyi Gong, Xiaojiang Du, and Miao Yu. "Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals." Applied Sciences 9, no. 1 (January 5, 2019): 175. http://dx.doi.org/10.3390/app9010175.

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Анотація:

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.

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30

Davies, Ross, Ian Wilson, and Andrew Ware. "Stereoscopic Human Detection in a Natural Environment." Annals of Emerging Technologies in Computing 2, no. 2 (April 1, 2018): 15–23. http://dx.doi.org/10.33166/aetic.2018.02.002.

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The algorithm presented in this paper is designed to detect people in real-time from 3D footage for use in Augmented Reality applications. Techniques are discussed that hold potential for a detection system when combined with stereoscopic video capture using the extra depth included in the footage. This information allows for the production of a robust and reliable system. To utilise stereoscopic imagery, two separate images are analysed, combined and the human region detected and extracted. The greatest benefit of this system is the second image, which contains additional information to which conventional systems do not have access, such as the depth perception in the overlapping field of view from the cameras. We describe the motivation behind using 3D footage and the technical complexity of human detection. The system is analysed for both indoor and outdoor usage, when detecting human regions. The developed system has further uses in the field of motion capture, computer gaming and augmented reality. Novelty comes from the camera not being fixed to a single point. Instead, the camera is subject to six degrees of freedom (DOF). In addition, the algorithm is designed to be used as a first filter to extract feature points in input video frames faster than real-time.

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31

Armengol, J., J. Vehi, M. A. Sainz, P. Herrero, and E. R. Gelso. "SQualTrack: A Tool for Robust Fault Detection." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 2 (April 2009): 475–88. http://dx.doi.org/10.1109/tsmcb.2008.2006909.

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32

SHIOMI, Kakuichi, Katsuhiko SHIMOYAMA, Shun TAKAMURA, Nobuhide ASO, and Yoshihiro SASAKI. "Robust Characteristics Defined on Human Voice for Detection of Fake Fatigued Voice." Japanese Journal of Ergonomics 56, Supplement (2020): 2D3–03–2D3–03. http://dx.doi.org/10.5100/jje.56.2d3-03.

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33

Sun, Yongliang, Katherine Yang, Terry Bridal, and Anka G. Ehrhardt. "Robust Ki67 detection in human blood by flow cytometry for clinical studies." Bioanalysis 8, no. 23 (December 2016): 2399–413. http://dx.doi.org/10.4155/bio-2016-0194.

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34

Park, Jin-Soo, and Han-Seok Ko. "Robust Speech Endpoint Detection in Noisy Environments for HRI (Human-Robot Interface)." Journal of the Acoustical Society of Korea 32, no. 2 (March 31, 2013): 147–56. http://dx.doi.org/10.7776/ask.2013.32.2.147.

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35

Jinhui Han, Yong Ma, Bo Zhou, Fan Fan, Kun Liang, and Yu Fang. "A Robust Infrared Small Target Detection Algorithm Based on Human Visual System." IEEE Geoscience and Remote Sensing Letters 11, no. 12 (December 2014): 2168–72. http://dx.doi.org/10.1109/lgrs.2014.2323236.

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36

Chuen, Benz Kek Yeo, Tee Connie, Ong Thian Song, and Michael Kah Ong Goh. "Partial Least Squares-Based Incremental PCA for Robust Human Detection and Tracking." Advanced Science Letters 24, no. 2 (February 1, 2018): 1052–56. http://dx.doi.org/10.1166/asl.2018.10685.

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37

Mateus, André, David Ribeiro, Pedro Miraldo, and Jacinto C. Nascimento. "Efficient and robust Pedestrian Detection using Deep Learning for Human-Aware Navigation." Robotics and Autonomous Systems 113 (March 2019): 23–37. http://dx.doi.org/10.1016/j.robot.2018.12.007.

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38

Wong, Kwok-Wai, Kin-Man Lam, and Wan-Chi Siu. "A robust scheme for live detection of human faces in color images." Signal Processing: Image Communication 18, no. 2 (February 2003): 103–14. http://dx.doi.org/10.1016/s0923-5965(02)00088-7.

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39

Tian, Luchao, Mingchen Li, Yu Hao, Jun Liu, Guyue Zhang, and Yan Qiu Chen. "Robust 3-D Human Detection in Complex Environments With a Depth Camera." IEEE Transactions on Multimedia 20, no. 9 (September 2018): 2249–61. http://dx.doi.org/10.1109/tmm.2018.2803526.

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40

How-Lung Eng, Junxian Wang, A. H. K. S. Wah, and Wei-Yun Yau. "Robust human detection within a highly dynamic aquatic environment in real time." IEEE Transactions on Image Processing 15, no. 6 (June 2006): 1583–600. http://dx.doi.org/10.1109/tip.2006.871119.

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41

Guo, Fan, Yuxiang Mai, Jin Tang, Yu Huang, and Lijun Zhu. "Robust and Automatic Skyline Detection Algorithm Based on MSSDN." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 6 (November 20, 2020): 750–62. http://dx.doi.org/10.20965/jaciii.2020.p0750.

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Анотація:

Automatic detection of the skyline plays an important role in several applications, such as visual geo-localization, flight control, port security, and mountain peak recognition. Existing skyline detection methods are mostly used under common weather conditions; however, they do not consider bad weather situations, such as rain, which limits their application in real scenes. In this paper, we propose a multi-stream-stage DenseNet to detect skyline automatically under different weather conditions. This model fully considers the adverse factors influencing the skyline and outputs a probability graph of the skyline. Finally, a dynamic programming algorithm is implemented to detect the skyline in images accurately. A comparison with the existing state-of-the-art methods proves that the proposed model shows a good performance under rainy or common weather conditions and exhibits the best detection precision for the public database.

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42

Wang, Xu, Linghua Zhang, Qin Cheng, and Feng Shu. "MoSeFi: Duration Estimation Robust Human Motion Sensing via Commodity WiFi Device." Wireless Communications and Mobile Computing 2022 (November 4, 2022): 1–19. http://dx.doi.org/10.1155/2022/1690602.

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Accurate motion interval segmentation is the basic and crucial step in the advanced human perception based on WiFi signals. However, previous works have rarely considered motion duration, which is one of the important parameters for complete description of human motion. On this basis, we deeply investigate the properties of the CSI ratio from the perspective of Mobius transformation and construct a novel motion indicator using its complementary real and imaginary parts. The new indicator can attenuate the impact of motion fragmentation under short-window conditions and significantly reduce the duration error while ensuring detection accuracy. Moreover, we propose a universal subcarrier screening method based on response sensitivity and shape similarity, which provides more accurate information for perception. Furthermore, we present MoSeFi—a duration estimation robust human motion detection system using an existing commercial WiFi device. Detailed experimental results demonstrate that MoSeFi is lightweight yet effective compared to state-of-the-art systems.

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43

Tan, Zheng-Hua, Achintya kr Sarkar, and Najim Dehak. "rVAD: An unsupervised segment-based robust voice activity detection method." Computer Speech & Language 59 (January 2020): 1–21. http://dx.doi.org/10.1016/j.csl.2019.06.005.

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44

Che, Sheng Bing, Jin Kai Luo, and Bin Ma. "Quasi-Blind Adaptive Video Watermarking Algorithm Based on Human Visual System." Advanced Materials Research 179-180 (January 2011): 103–8. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.103.

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A quasi-blind adaptive video watermarking algorithm in uncompressed video wavelet domain based on human visual system is proposed in this paper. This algorithm established a visual model according to the human visual masking and the characteristics of wavelet coefficients. It did not require the scene segmentation of the video, choosing the video frames which used for embedding watermarking by the key, and embedding different watermarking which processed by Arnold scrambling into different frame, so it is robust to statistical analysis, frame crop and so on. To ensure the spatial synchrony when extracting the watermark information, zero-watermarking method and chaotic system are used to generate the synchronization information. The quantized central limit theorem is applied to adjust the low frequency coefficients, it makes the extracted watermark information keeps invariant when its element value changed in robust region. A new correlation detection method of watermarking information was put forward. Watermark detection does not require original video. It realized the quasi-blind watermark detection.

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45

Zhang, Zinan, Zhanjun Hao, Xiaochao Dang, and Kaikai Han. "TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device." Applied Sciences 13, no. 17 (August 26, 2023): 9668. http://dx.doi.org/10.3390/app13179668.

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Анотація:

With the popularization of Wi-Fi router devices, the application of device-free sensing has garnered significant attention due to its potential to make our lives more convenient. Wi-Fi signal-based through-the-wall human detection offers practical applications, such as emergency rescue and elderly monitoring. However, the accuracy of through-the-wall human detection is hindered by signal attenuation caused by wall materials and multiple propagation paths of interference. Therefore, through-the-wall human detection presents a substantial challenge. In this paper, we proposed a highly robust through-the-wall human detection method based on a commercial Wi-Fi device (TwSense). To mitigate interference from wall materials and other environmental factors, we employed the robust principal component analysis (OR-PCA) method to extract the target signal of Channel State Information (CSI). Subsequently, we segmented the action-induced Doppler shift feature image using the K-means clustering method. The features of the images were extracted using the Histogram of Oriented Gradients (HOG) algorithm. Finally, these features were fed into an SVM classifier (G-SVM) optimized by a grid search algorithm for action classification and recognition, thereby enhancing human detection accuracy. We evaluated the robustness of the entire system. The experimental results demonstrated that TwSense achieved the highest accuracy of 96%.

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46

Amjed, Noor, Fatimah Khalid, Rahmita Wirza O. K. Rahmat, and Hizmawati Bint Madzin. "A Robust Geometric Skin Colour Face Detection Method under Unconstrained Environment of Smartphone Database." Applied Mechanics and Materials 892 (June 2019): 31–37. http://dx.doi.org/10.4028/www.scientific.net/amm.892.31.

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Face detection is the primary task in building a vision-based human-computer interaction system and in special applications such as face recognition, face tracking, face identification, expression recognition and also content-based image retrieval. A potent face detection system must be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, orientation and facial expressions. In previous literature, many approaches for face detection had been proposed. However, face detection in outdoor images with uncontrolled illumination and images with complex background are still a serious problem. Hence, in this paper, we had proposed a Geometric Skin Colour (GSC) method for detecting faces accurately in real world image, under capturing conditions of both indoor and outdoor, and with a variety of illuminations and also in cluttered backgrounds. The selected method was evaluated on two different face video smartphone databases and the obtained results proved the outperformance of the proposed method under the unconstrained environment of these databases.

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47

Jia, Lin Bin, Lin Li, and Rong Nie. "Acoustic Events Detection in Dissimilarity Measurement Space." Applied Mechanics and Materials 333-335 (July 2013): 764–68. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.764.

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The paper considers the problem of detecting acoustic events in a robust manner. The dissimilarity measurement is used to measure the distance between acoustic samples. Then this distance is used as the replacement of the Euclidean distance to build the detection model with the SVM algorithm. All the well-known features are considered when we build model in a way of feature subset ensemble. Experiments are conducted to detect events under a variety of environmental sounds. The model demonstrates the robustness of the ensemble method with dissimilarity measurement. The detection model has shown to produce comparable performance as human listeners.

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PHAM-NGOC, PHUONG-TRINH, TAE-HO KIM, and KANG-HYUN JO. "ROBUST FACE DETECTION FOR MOVING PICTURES UNDER POSE, ROTATION, ILLUMINATION AND OCCLUSION CHANGES." International Journal of Information Acquisition 04, no. 04 (December 2007): 291–302. http://dx.doi.org/10.1142/s0219878907001368.

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Анотація:

Face detection has been a key step in face analysis systems for decades. However, it is still a challenging task due to the variation in image background, view, pose, occlusion, etc. This paper proposes a simple and effective tool to detect human faces in moving pictures under such conditions. An improved approach aiming to reduce impacts of illumination, scale and connection of faces to receive rapidly skin homogeneous regions considered as the most potential face candidates is presented. A hybrid classifier, applied in retrieved face candidates, is based on template matching and appearance-based method providing a robust face detection. This verification achieves advantages of the powerful discrimination of Local Binary Patterns (LBPs) and the high speed detection capability of embedded Hidden Markov Models (eHMMs). Experiments were performed with different image databases and video sequences such as NRC-IIT facial video database, Caltech database, etc. Our system is effective in detecting not only frontal faces but also profile, rotated, occluded and connected ones for real-time application.

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49

Flachner, Beáta, Krisztina Dobi, Anett Benedek, Sándor Cseh, Zsolt Lőrincz, and István Hajdú. "Robust Recombinant Expression of Human Placental Ribonuclease Inhibitor in Insect Cells." Biomolecules 12, no. 2 (February 8, 2022): 273. http://dx.doi.org/10.3390/biom12020273.

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Ribonuclease inhibitors (RIs) are an indispensable biotechnological tool for the detection and manipulation of RNA. Nowadays, due to the outbreak of COVID-19, highly sensitive detection of RNA has become more important than ever. Although the recombinant expression of RNase inhibitors is possible in E. coli, the robust expression is complicated by maintaining the redox potential and solubility by various expression tags. In the present paper we describe the expression of RI in baculovirus-infected High Five cells in large scale utilizing a modified transfer vector combining the beneficial properties of Profinity Exact Tag and pONE system. The recombinant RI is expressed at a high level in a fusion form, which is readily cleaved during on-column chromatography. A subsequent anion exchange chromatography was used as a polishing step to yield 12 mg native RI per liter of culture. RI expressed in insect cells shows higher thermal stability than the commercially available RI products (mainly produced in E. coli) based on temperature-dependent RNase inhibition studies. The endotoxin-free RI variant may also be applied in future therapeutics as a safe additive to increase mRNA stability in mRNA-based vaccines.

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50

Wu, Wenyan, Xingzhe Wu, Yici Cai, and Qiang Zhou. "Deep coupling neural network for robust facial landmark detection." Computers & Graphics 82 (August 2019): 286–94. http://dx.doi.org/10.1016/j.cag.2019.05.031.

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