RETRACTED: Gait Analysis of Pedestrians with the Aim of Detecting Disabled People

Gait classification is an effective and non-intrusive method for human identification and it has received significant attention in the recent years due to its applications in visual surveillance and monitoring systems. In this project, we analysed gait signatures using spatio-temporal motion characteristics of a person to answer the question ``is there a discriminating feature in gait signal that can help to categorise disable person from healthy''. The procedure has three steps. detection of a pedestrian using YOLO followed by the silhouette extraction using the Gaussian Mixture Model (GMM). Finally, skeletonization from the silhouette image to estimate head and torso locations and their angles with the vertical axis. Furthermore, velocity and acceleration signals were recorded to look for accelerating behaviour of person walking with a limp.

[1]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[2]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[4]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[5]  Toshi Takamori,et al.  Human detection and localization at indoor environment by home robot , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[6]  Larry S. Davis,et al.  EigenGait: Motion-Based Recognition of People Using Image Self-Similarity , 2001, AVBPA.

[7]  Larry S. Davis,et al.  Stride and cadence as a biometric in automatic person identification and verification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[8]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Rama Chellappa,et al.  Identification of humans using gait , 2004, IEEE Transactions on Image Processing.

[10]  Tim J. Ellis,et al.  Learning semantic scene models from observing activity in visual surveillance , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Rama Chellappa,et al.  Applications of a Simple Characterization of Human Gait in Surveillance , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Hélène Laurent,et al.  Comparative study of background subtraction algorithms , 2010, J. Electronic Imaging.

[14]  Philippe De Wilde,et al.  Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics , 2010, International Journal of Computer Vision.

[15]  Kejun Wang,et al.  Video-Based Abnormal Human Behavior Recognition—A Review , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Saeid Sanei,et al.  A comprehensive review of past and present vision-based techniques for gait recognition , 2013, Multimedia Tools and Applications.

[17]  Manoranjan Paul,et al.  Human detection in surveillance videos and its applications - a review , 2013, EURASIP J. Adv. Signal Process..

[18]  Meng Wang,et al.  Deep Learning of Scene-Specific Classifier for Pedestrian Detection , 2014, ECCV.

[19]  B. Burdett.  Measuring Accessible Journeys , 2014 .

[20]  Ahmed Bouridane,et al.  Improved Human Gait Recognition , 2015, ICIAP.

[21]  Paulo Cortez,et al.  Automatic visual detection of human behavior: A review from 2000 to 2014 , 2015, Expert Syst. Appl..

[22]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.