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Journal articles on the topic 'TRACKING THE HUMAN BODY'

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Author: Grafiati

Published: 11 September 2023

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1

Zhang, Gang, Bin Ouyang, Lu Ming Yu, and Lei Zhang. "Research of Human Body Detection and Tracking Algorithm." Advanced Materials Research 791-793 (September 2013): 1023–27. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1023.

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In this paper, the proposed algorithm regards the human body object character symbol using Support Vector Machine (SVM) classifier to train and classify Histogram of Oriented Gradient (HOG) features, which improve the accuracy of human body detection. We use optical flow tracking algorithm based on corner points of the contour for tracking. Kalman filter is regarded as the predictor to predict the size and location of the searching object. Also, the size and location of track window is real-time updated. In this paper, we present an object tracking algorithm for multi-media teaching video shoot. Target tracking technology is used for the video image processing analysis. By extracting moving object, we can get information in the subsequent frames to determine the trajectory and size of moving objects. After analysis of a large number of experiments, we can draw the conclusion that the algorithm is effective.

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2

Wren, C. R., A. Azarbayejani, T. Darrell, and A. P. Pentland. "Pfinder: real-time tracking of the human body." IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 7 (July 1997): 780–85. http://dx.doi.org/10.1109/34.598236.

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3

Jang, Dae-Sik, Seok-Woo Jang, and Hyung-Il Choi. "2D human body tracking with Structural Kalman filter." Pattern Recognition 35, no. 10 (October 2002): 2041–49. http://dx.doi.org/10.1016/s0031-3203(01)00201-1.

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4

Yu, Jie, FengLi Zhang, Jian Xiong, and GuoCheng Yang. "A Robust Real-Time Human Body Fuzzy Tracking Based Face Tracking Algorithm." Journal of Computational and Theoretical Nanoscience 12, no. 12 (December 1, 2015): 5728–38. http://dx.doi.org/10.1166/jctn.2015.4709.

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5

Wang, Jun Jie. "The Visual Simulation Analysis of Human Body Movement Model." Applied Mechanics and Materials 556-562 (May 2014): 3913–16. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3913.

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This paper proposes the re-built human body movement model with multiple cameras. In the tracking frame of the non-linear optimization strategy, the paper builds the body dynamic model to dynamically simulate the human movement which effectively solves the issues of the body parts overlap and tracking errors accumulate. Compared with traditional methods, the required equipment is very economic and the matching accuracy of the algorithm is quite high. The paper applies the athletes as the experimental examples which illustrate the proposed algorithm can effectively increase the 3D image tracking matching accuracy in dynamic videos as the analysis basis.

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6

Polat, Ediz, Mohammed Yeasin, and Rajeev Sharma. "Robust tracking of human body parts for collaborative human computer interaction." Computer Vision and Image Understanding 89, no. 1 (January 2003): 44–69. http://dx.doi.org/10.1016/s1077-3142(02)00031-0.

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7

KHONGKRAPHAN, Kittiya, and Pakorn KAEWTRAKULPONG. "Efficient Human Body Tracking by Quick Shift Belief Propagation." IEICE Transactions on Information and Systems E94-D, no. 4 (2011): 905–12. http://dx.doi.org/10.1587/transinf.e94.d.905.

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8

Zhou, Yi. "Bayesian variational human tracking based on informative body parts." Optical Engineering 51, no. 6 (June 5, 2012): 067203. http://dx.doi.org/10.1117/1.oe.51.6.067203.

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9

Herda, L., R. Urtasun, and P. Fua. "Hierarchical implicit surface joint limits for human body tracking." Computer Vision and Image Understanding 99, no. 2 (August 2005): 189–209. http://dx.doi.org/10.1016/j.cviu.2005.01.005.

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10

Cao, Xiao-Qin, and Zhi-Qiang Liu. "Sequential Markov random fields for human body parts tracking." Multimedia Tools and Applications 74, no. 17 (May 14, 2014): 6671–90. http://dx.doi.org/10.1007/s11042-014-1924-3.

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11

Yang, Ming. "Tracking human fat turnover with carbon dating." Science Translational Medicine 11, no. 511 (September 25, 2019): eaaz4961. http://dx.doi.org/10.1126/scitranslmed.aaz4961.

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12

Man, Jun Feng, Hai Yu Lin, Qian Qian Li, and Xiang Bing Wen. "Segmentation and Tracking of Human in Crowded Environments." Advanced Materials Research 255-260 (May 2011): 2281–85. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2281.

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The occlusion problem in crowded people environment makes human segmentation and tracking more difficult in video surveillance. Thus, a human segmentation method combing human model with body edge curve is presented. Because segmentation may result in serious defect and distortion, robust BP neural network model is adopted as tracking mode. For improving autonomous learning ability of BP network, Hierarchical Dirichlet Process (HDP) is used to decide whether new types of human body characteristic data is generated, which provides decision basis for BP network learning. The simulation experiments confirm that the method presented in this paper can effectively solve the problem of partial human body occlusion. Meanwhile, this method has unique advantage of simplicity and real-time over others.

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13

Geng, Yishuang, Jie He, and Kaveh Pahlavan. "Modeling the Effect of Human Body on TOA Based Indoor Human Tracking." International Journal of Wireless Information Networks 20, no. 4 (September 13, 2013): 306–17. http://dx.doi.org/10.1007/s10776-013-0227-3.

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14

Liu, Qiong, and Guang Zheng Peng. "Particle Filter with Constraints for Articulated Upper Body Tracking." Key Engineering Materials 439-440 (June 2010): 971–76. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.971.

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For sophisticated background, a human body tracking algorithm using particle filter based on a 3D articulated body model is introduced. First, a high-fidelity biomechanical upper body model, which is accurate for representing varies complicated human poses and simple to be developed, has been built. Then sequences of images are obtained by using a stereo camera. After calibration, verification and background subtraction, depth map, foreground silhouette, arms skeleton are chosen to construct the likelihood function. The state vectors describing the human pose are computed by fitting the articulated body model to observed person using particle filter. In order to reduce the computational complexity and the number of particles, constraints are employed to restrict the state parameters. Experimental results show that the proposed algorithm can track human upper body with different poses, different person under different illumination conditions fast and accurately.

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15

Fajrianti, Evianita Dewi, Sritrusta Sukaridhoto, M. Udin Harun Al Rasyid, Bambang Edi Suwito, Rizqi Putri Nourma Budiarti, Ilham Achmad Al Hafidz, Naufal Adi Satrio, and Amma Liesvarastranta Haz. "Application of Augmented Intelligence Technology with Human Body Tracking for Human Anatomy Education." International Journal of Information and Education Technology 12, no. 6 (2022): 476–84. http://dx.doi.org/10.18178/ijiet.2022.12.6.1644.

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Technological developments have a positive impact on humans to improve their abilities in line with innovations in the field of education which are increasing day by day. One of them is by increasing intelligence through collaboration between humans and technology called augmented intelligence. In this study augmented intelligence is applied to assist humans in studying human anatomy by utilizing augmented reality technology to perform motion tracking that can control 3D assets to follow it. The anatomy learning platform that was built was named AIVE (Artificial Intelligence on Virtual Education) with an architectural system consisting of a frontend for interface needs and AR platform development, there is also a backend for streaming databases and AI algorithms. Measurement of satisfaction and interest based on the PIECES framework is also carried out to determine the user’s response to the platform being built. The results show a satisfaction level of 4.12 and an interest of 4.10 which means that users are satisfied and interested in the human anatomy tracking platform that is accessed via smartphones.

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16

Abdellaoui, Mehrez, and Ali Douik. "Template matching approach for automatic human body tracking in video." International Journal of Intelligent Engineering Informatics 6, no. 5 (2018): 434. http://dx.doi.org/10.1504/ijiei.2018.094508.

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Douik, Ali, and Mehrez Abdellaoui. "Template matching approach for automatic human body tracking in video." International Journal of Intelligent Engineering Informatics 6, no. 5 (2018): 434. http://dx.doi.org/10.1504/ijiei.2018.10015601.

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18

Hwang, Seung-Jun, Jae-Hong Min, In-Gyu Kim, Seung-Jae Park, Gwang-Pyo Ahn, and Joong-Hwan Baek. "Human Body Tracking and Pose Estimation Using Modified Camshift Algorithm." Journal of Software Engineering and Applications 06, no. 05 (2013): 37–42. http://dx.doi.org/10.4236/jsea.2013.65b008.

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19

Choi, H. R., M. H. Ryu, and Y. S. Yang. "Human Body Orientation Tracking System Using Inertial and Magnetic Sensors." Journal of Biomedical Engineering Research 32, no. 2 (April 30, 2011): 118–26. http://dx.doi.org/10.9718/jber.2011.32.2.118.

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20

Guo, Yan, Gang Xu, and Saburo Tsuji. "Tracking Human Body Motion Based on a Stick Figure Model." Journal of Visual Communication and Image Representation 5, no. 1 (March 1994): 1–9. http://dx.doi.org/10.1006/jvci.1994.1001.

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21

Xie, Liang, Xiaohu Zhang, Yuhua Xu, Yang Shang, and QiFeng Yu. "SkeletonFusion: Reconstruction and tracking of human body in real-time." Optics and Lasers in Engineering 110 (November 2018): 80–88. http://dx.doi.org/10.1016/j.optlaseng.2018.05.011.

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22

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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23

Moon, H., and R. Chellappa. "3D Shape-Encoded Particle Filter for Object Tracking and Its Application to Human Body Tracking." EURASIP Journal on Image and Video Processing 2008 (2008): 1–16. http://dx.doi.org/10.1155/2008/596989.

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24

O’Hara, Kenton, Abigail Sellen, and Juan Wachs. "Introduction to Special Issue on Body Tracking and Healthcare." Human–Computer Interaction 31, no. 3-4 (February 9, 2016): 173–90. http://dx.doi.org/10.1080/07370024.2016.1151712.

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25

SNIDARO, LAURO, GIAN LUCA FORESTI, and LUCA CHITTARO. "TRACKING HUMAN MOTION FROM MONOCULAR SEQUENCES." International Journal of Image and Graphics 08, no. 03 (July 2008): 455–71. http://dx.doi.org/10.1142/s0219467808003180.

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In recent years, analysis of human motion has become an increasingly relevant research topic with applications as diverse as animation, virtual reality, security, and advanced human-machine interfaces. In particular, motion capture systems are well known nowadays since they are used in the movie industry. These systems require expensive multi-camera setups or markers to be worn by the user. This paper describes an attempt to provide a markerless low cost and real-time solution for home users. We propose a novel approach for robust detection and tracking of the user's body joints that exploits different algorithms as different sources of information and fuses their estimates with particle filters. This system may be employed for real-time animation of VRML or X3D avatars using an off-the-shelf digital camera and a standard PC.

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26

Azhar, Faisal, and Tardi Tjahjadi. "Significant Body Point Labeling and Tracking." IEEE Transactions on Cybernetics 44, no. 9 (September 2014): 1673–85. http://dx.doi.org/10.1109/tcyb.2014.2303993.

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27

Khan, Muhammad Hassan, Martin Zöller, Muhammad Shahid Farid, and Marcin Grzegorzek. "Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure." Sensors 20, no. 11 (June 10, 2020): 3312. http://dx.doi.org/10.3390/s20113312.

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Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints’ locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints’ locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.

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28

Ren, Yili, Zi Wang, Sheng Tan, Yingying Chen, and Jie Yang. "Winect." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (December 27, 2021): 1–29. http://dx.doi.org/10.1145/3494973.

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WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, we combine signal separation and joint movement modeling to achieve free-form activity tracking. Our system first identifies the moving limbs by leveraging the two-dimensional angle of arrival of the signals reflected off the human body and separates the entangled signals for each limb. Then, it tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect is environment-independent and achieves centimeter-level accuracy for free-form activity tracking under various challenging environments including the none-line-of-sight (NLoS) scenarios.

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29

Feng, Xingyang, Qingbin Wang, Hua Cong, Yu Zhang, and Mianhao Qiu. "Gaze Point Tracking Based on a Robotic Body–Head–Eye Coordination Method." Sensors 23, no. 14 (July 11, 2023): 6299. http://dx.doi.org/10.3390/s23146299.

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When the magnitude of a gaze is too large, human beings change the orientation of their head or body to assist their eyes in tracking targets because saccade alone is insufficient to keep a target at the center region of the retina. To make a robot gaze at targets rapidly and stably (as a human does), it is necessary to design a body–head–eye coordinated motion control strategy. A robot system equipped with eyes and a head is designed in this paper. Gaze point tracking problems are divided into two sub-problems: in situ gaze point tracking and approaching gaze point tracking. In the in situ gaze tracking state, the desired positions of the eye, head and body are calculated on the basis of minimizing resource consumption and maximizing stability. In the approaching gaze point tracking state, the robot is expected to approach the object at a zero angle. In the process of tracking, the three-dimensional (3D) coordinates of the object are obtained by the bionic eye and then converted to the head coordinate system and the mobile robot coordinate system. The desired positions of the head, eyes and body are obtained according to the object’s 3D coordinates. Then, using sophisticated motor control methods, the head, eyes and body are controlled to the desired position. This method avoids the complex process of adjusting control parameters and does not require the design of complex control algorithms. Based on this strategy, in situ gaze point tracking and approaching gaze point tracking experiments are performed by the robot. The experimental results show that body–head–eye coordination gaze point tracking based on the 3D coordinates of an object is feasible. This paper provides a new method that differs from the traditional two-dimensional image-based method for robotic body–head–eye gaze point tracking.

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30

Qian, Huizu, Benbin Chen, Xuke Xia, Shengzhong Deng, and Yuxiang Wang. "D-H Parameter Method-based Wearable Motion Tracking." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2216/1/012027.

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Abstract Motion capture is a key technology for robots to accurately understand pedestrian intentions in the scene of human-machine integration. Due to the limited spatial distance and easy obstruction by obstacles, traditional optical motion capture systems often lose detection targets. This paper proposes a wearable motion tracking method based on D-H parameter method. By binding multiple wireless inertial sensor units composed of accelerometers, magnetic flux sensors and gyroscopes to various moving parts of the user’s body, accurate and robust tracking of moving targets is achieved. This method uses the known pose information of the root node to find the pose state of each level in the reference coordinate system, and establishes the human body joint rotation model and the bone position state model. The results show that the motion tracking method proposed in this paper reduces 9 degrees of freedom compared with the traditional forward kinematics method, and the algorithm efficiency is increased by about 20%, which can accurately obtain the posture characteristics of the human body. It can be seen that the D-H parameter method is reasonable for the wearable human body motion tracking.

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31

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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32

Ribeiro, Pedro Manuel Santos, Ana Clara Matos, Pedro Henrique Santos, and Jaime S. Cardoso. "Machine Learning Improvements to Human Motion Tracking with IMUs." Sensors 20, no. 21 (November 9, 2020): 6383. http://dx.doi.org/10.3390/s20216383.

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Inertial Measurement Units (IMUs) have become a popular solution for tracking human motion. The main problem of using IMU data for deriving the position of different body segments throughout time is related to the accumulation of the errors in the inertial data. The solution to this problem is necessary to improve the use of IMUs for position tracking. In this work, we present several Machine Learning (ML) methods to improve the position tracking of various body segments when performing different movements. Firstly, classifiers were used to identify the periods in which the IMUs were stopped (zero-velocity detection). The models Random Forest, Support Vector Machine (SVM) and neural networks based on Long-Short-Term Memory (LSTM) layers were capable of identifying those periods independently of the motion and body segment with a substantially higher performance than the traditional fixed-threshold zero-velocity detectors. Afterwards, these techniques were combined with ML regression models based on LSTMs capable of estimating the displacement of the sensors during periods of movement. These models did not show significant improvements when compared with the more straightforward double integration of the linear acceleration data with drift removal for translational motion estimate. Finally, we present a model based on LSTMs that combined simultaneously zero-velocity detection with the translational motion of sensors estimate. This model revealed a lower average error for position tracking than the combination of the previously referred methodologies.

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33

Qi, Su Min, and Ran Xu. "Application of Color Transfer Algorithm in the Virtual Color Restoration of Ancient Architecture." Applied Mechanics and Materials 321-324 (June 2013): 2291–95. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2291.

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An automatic human motion tracking algorithm based the connection between the improved Snake model and optical flow arithmetic is proposed. The initial contour most close to human body is acquired by corner detection arithmetic, which decreases the iterations and reduces the probability of converging at local extreme value of Snake model. Because the tracking with Snake model is unstable and often loses the object, the good feature points are chosen from the contour points in current frame for optical flow estimation. Then the result is chosen as the initial contour in next frame. Experimental results show that improved Snake model can be deformed to the actual human body contour, and the automatic and real-time human tracking is implemented.

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34

LONG, WARREN, and YEE-HONG YANG. "LOG-TRACKER: AN ATTRIBUTE-BASED APPROACH TO TRACKING HUMAN BODY MOTION." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 03 (August 1991): 439–58. http://dx.doi.org/10.1142/s0218001491000259.

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Motion provides extra information that can aid in the recognition of objects. One of the most commonly seen objects is, perhaps, the human body. Yet little attention has been paid to the analysis of human motion. One of the key steps required for a successful motion analysis system is the ability to track moving objects. In this paper, we describe a new system called Log-Tracker, which was recently developed for tracking the motion of the different parts of the human body. Occlusion of body parts is termed a forking condition. Two classes of forks as well as the attributes required to classify them are described. Experimental results from two gymnastics sequences indicate that the system is able to track the body parts even when they are occluded for a short period of time. Occlusions that extend for a long period of time still pose problems to Log-Tracker.

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35

Ou Pan, 欧攀, 吴帅 Wu Shuai, and 周锴 Zhou Kai. "Fast Human Body Measurement Method Based on Depth Sensor Skeleton Tracking." Laser & Optoelectronics Progress 54, no. 12 (2017): 121206. http://dx.doi.org/10.3788/lop54.121206.

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36

Rius, Ignasi, Jordi Gonzàlez, Javier Varona, and F. Xavier Roca. "Action-specific motion prior for efficient Bayesian 3D human body tracking." Pattern Recognition 42, no. 11 (November 2009): 2907–21. http://dx.doi.org/10.1016/j.patcog.2009.02.012.

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37

Urtasun, Raquel, David J. Fleet, and Pascal Fua. "Temporal motion models for monocular and multiview 3D human body tracking." Computer Vision and Image Understanding 104, no. 2-3 (November 2006): 157–77. http://dx.doi.org/10.1016/j.cviu.2006.08.006.

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38

Peursum, Patrick, Svetha Venkatesh, and Geoff West. "A Study on Smoothing for Particle-Filtered 3D Human Body Tracking." International Journal of Computer Vision 87, no. 1-2 (January 28, 2009): 53–74. http://dx.doi.org/10.1007/s11263-009-0205-5.

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39

Daehwan Kim and Daijin Kim. "A Fast ICP Algorithm for 3-D Human Body Motion Tracking." IEEE Signal Processing Letters 17, no. 4 (April 2010): 402–5. http://dx.doi.org/10.1109/lsp.2009.2039888.

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40

Bao, Hong, and Zhi Min Liu. "Video-Based Human Motion Analysis." Advanced Materials Research 403-408 (November 2011): 2593–97. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2593.

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In the analysis of human motion, movement was divided into regular motion (such as walking and running) and random motion (such as falling down).Human skeleton model is used in this paper to do the video-based analysis. Key joints on human body were chosen to be traced instead of tracking the entire human body. Shape features like mass center trajectory were used to describe the movement, and to classify human motion. desired results achieved.

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41

Dai, K. X., H. Guo, P. Mordohai, F. Marinello, A. Pezzuolo, Q. L. Feng, and Q. D. Niu. "NON-RIGID MULTI-BODY TRACKING IN RGBD STREAMS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 341–48. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-341-2019.

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<p><strong>Abstract.</strong> To efficiently collect training data for an off-the-shelf object detector, we consider the problem of segmenting and tracking non-rigid objects from RGBD sequences by introducing the spatio-temporal matrix with very few assumptions &ndash; no prior object model and no stationary sensor. Spatial temporal matrix is able to encode not only spatial associations between multiple objects, but also component-level spatio temporal associations that allow the correction of falsely segmented objects in the presence of various types of interaction among multiple objects. Extensive experiments over complex human/animal body motions with occlusions and body part motions demonstrate that our approach substantially improves tracking robustness and segmentation accuracy.</p>

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42

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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43

Hu, Hai, Bin Li, Ben Xiong Huang, and Xiao Lei He. "Key Points of Human Body Location Based on Single Depth Map." Applied Mechanics and Materials 203 (October 2012): 76–82. http://dx.doi.org/10.4028/www.scientific.net/amm.203.76.

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This paper presents a method of using single depth map to locate the key points of frontal human body. Human motion capture is the premise of motion analysis and understanding, and it has widely application prospects. There are many problems on former way to capture the state of human motion. For example, it can’t initialize automatically, it can not recover from tracking failure, it can not solve the problem caused by occlusion, or there are many constraints on participant, and so on. This article uses Kinect, which from Microsoft, to get depth maps, and use a single map as input to locate the key points of human body. First, depth map can reflect the distance, so background segmentation can be done easily by the characteristic. Then, extract the skeleton of the body’s silhouette. Finally, using the inherent connectivity features of human body, the key points of the body can be determined on the skeleton. Locating the key points from single depth map solve the problem of automatic initialization and recovery directly. The depth map can reflect distance on grayscale, which makes it easy to split the body region from the background. In addition, depth map contains some useful information can be used to solve the problem of occlusion. Using depth map can remove some constraints on the human body, as well as to reduce the influence of clothing and surround lighting, and so on. The experiment shows that this method is very accurate in locating the key points of frontal stand human body, and can solve some problems of occlusion. It is ideal used in a motion tracking system for automatic initialization and self-recovery when tracking failed

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Yang, Ning, Jin Tao Li, and Rong Wang. "A Method of Lower Limb Joint Points Extraction Based on Pendulum Model under Arbitrary Gesture Walk." Applied Mechanics and Materials 556-562 (May 2014): 4347–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4347.

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The position extraction of lower limb joint points is important for gait recognition because the feature data is always based on the position of lower limb joint points. Since the detection of motion information of human body can affect the gait recognition directly, we propose a position extraction method of lower limb joint points in this paper. Through the study on the human body centroid tracking, and positioning of human lower limb joint point, we can obtain the step cycle information. It has been demonstrated via plenty experiments that the proposed method is feasible and easy for implement, since it can achieve real-time tracking and improve positioning accuracy of the human body joints, and can provide feature data for human gait recognition.

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Xu, Cheng, Jie He, Xiaotong Zhang, Xinghang Zhou, and Shihong Duan. "Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits." Electronics 8, no. 2 (January 29, 2019): 142. http://dx.doi.org/10.3390/electronics8020142.

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Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

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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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Karlgren, Kasper, Barry Brown, and Donald McMillan. "From Self-Tracking to Sleep-Hacking." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–26. http://dx.doi.org/10.1145/3555630.

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With growing interest in how technology can make sense of our body and bodily experiences, this work looks at how these experiences are communicated through and with the help of technology. We present the ways in which knowledge about sleep, and how to manipulate it, is collectively shared online. This paper documents the sleep-change practices of four groups of 'Sleep Hackers' including Nurses, Polyphasic Sleeper, Over-sleepers, and Biohackers. Our thematic analysis uses 1002 posts taken from public forums discussing sleep change. This work reveals the different ways individuals share their experiences and build communal knowledge on how to 'hack' their sleep -- from using drugs, external stimulation, isolation, and polyphasic sleeping practices where segmented sleep schedules are shared between peers. We describe how communal discussions around the body and sleep can inform the development of body sensing technology. We discuss the opportunities and implications for designing for bodily agency over sleep changes both in relation to collaboratively developed understandings of the body and social context of the user. We also discuss notions of slowly changing bodily processes and sensory manipulation in relation to how they can build on the exploration of soma-technology.

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Huang, Yi Hu, Ji Xiang Ma, Xiao Dong Han, Ning Hu, and Xi Mei Jia. "Design of Human Tracking Algorithm Based on Improved Camshift." Key Engineering Materials 561 (July 2013): 677–82. http://dx.doi.org/10.4028/www.scientific.net/kem.561.677.

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Considering the problem of added background interference when initial target region is chose in Camshift tracking algorithm. This paper proposes a human tracking algorithm based on improved Camshift. The algorithm uses weight to determine the type of pixels in the back-projection, and then convert the back-projection into binary image, so as to improve the input accuracy of Camshift processing function. According to the human body size information, the algorithm appropriately improves the ratio of major axis and minor axis of search window, to optimize the output size of Camshift processing function. The contrast experiment proves the algorithm obviously improve accuracy and robustness of tracking effect.

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Zhang, Zhiliang. "Image Tracking and Detection of Standing Posture of Moving Human Body Based on The Recognition Algorithm of Human Body Structure." Journal of Physics: Conference Series 1992, no. 2 (August 1, 2021): 022151. http://dx.doi.org/10.1088/1742-6596/1992/2/022151.

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Sierotowicz, Marek, Mathilde Connan, and Claudio Castellini. "Human-In-The-Loop Assessment of an Ultralight, Low-Cost Body Posture Tracking Device." Sensors 20, no. 3 (February 7, 2020): 890. http://dx.doi.org/10.3390/s20030890.

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In rehabilitation, assistive and space robotics, the capability to track the body posture of a user in real time is highly desirable. In more specific cases, such as teleoperated extra-vehicular activity, prosthetics and home service robotics, the ideal posture-tracking device must also be wearable, light and low-power, while still enforcing the best possible accuracy. Additionally, the device must be targeted at effective human-machine interaction. In this paper, we present and test such a device based upon commercial inertial measurement units: it weighs 575 g in total, lasts up to 10.5 h of continual operation, can be donned and doffed in under a minute and costs less than 290 EUR. We assess the attainable performance in terms of error in an online trajectory-tracking task in Virtual Reality using the device through an experiment involving 10 subjects, showing that an average user can attain a precision of 0.66 cm during a static precision task and 6.33 cm while tracking a moving trajectory, when tested in the full peri-personal space of a user.

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