A Survey of Gait Recognition Approaches Using PCA

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait Analysis (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. It has been receiving wide attention in the area of Biometric. In Gait biometric research there are various gait recognition approaches are available. In this paper, the gait recognition approaches such as 201C;Wavelet Descriptor with ICA201D;, and 201C;Hough transform with PCA201D; are compared and discussed

A review of vision-based gait recognition methods for human identification

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field

Human Identification Using Gait

Keeping in view the growing importance of biometric signatures in automated security and surveillance systems, human gait recognition provides a low-cost non-obtrusive method for reliable person identification and is a promising area for research. This work employs a gait recognition process with binary silhouette-based input images and Hidden Markov Model (HMM)-based classification. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the HMM based classifier which uses Viterbi decoding and Baum-Welch algorithm to compute similarity scores and carry out identification. The direct method uses extracted wavelet features directly for classification while the indirect method maps the higher-dimensional features into a lower dimensional space by means of a Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined exemplars and the feature vectors of the current frame as an identification criterion. This work achieves an overall sensitivity of 86.44 % and 71.39 % using the direct and indirect approaches respectively. Also, variation in recognition performance is observed with change in the viewing angle and N and optimal performance is obtained when the path of subject parallel to camera axis (viewing angle of 0 degree) and at N = 5. The maximum recognition accuracy levels of 86.44 % and 80.93 % with and without FDEI reconstruction respectively also demonstrate the significance of FDEI reconstruction step

AN ANGULAR TRANSFORM OF GAIT SEQUENCES FOR GAIT ASSISTED RECOGNITION

ABSTRACT A new system is proposccd for gait analysis and recognition applications. 'The new system is based on a dcnoising process and a new angular transform that are applicd on hinary silhouettes. Each human silhouette in a pait sequencc is transformed into a low dinicnsional feature vector consisting of average pixel distances from the center OS the silhouette. The sequence of feature vectors corresponding to a gait sequence is used for identification based on a minimum-distance criterion between test and refercncc scqucnces. By using the new system on the Gait Challenge database. improvements in rccognition performance are seen in comparison to other methods of similar or higher complexity

Video-based step measurement in sport and daily living.

Current knowledge of tennis player-surface interactions with different court surfaces is limited. The measurement of player step and movement strategy would aid the understanding of tennis player-surface interaction. However, this has not yet been performed: no readily available motion analysis tool is capable of measuring spatio-temporal parameters of gait during match-play tennis. The purpose of this project was to develop, validate and use a motion analysis tool designed to measure player location and foot-surface contacts during match-play tennis.Single camera video footage, obtained from the 2011 Roland Garros Qualifying Tournament, was manually digitised to characterise step and movement strategy during men's and women's forehand groundstrokes. Player movements were consistent with previous notational analyses; however gender differences were highlighted for step frequency. Initial findings were limited by manual analysis, e.g. manual digitising subjectivity and low sample size: an objective and automated system was required.A markerless, view-independent, foot-surface contact identification (FSCi) algorithm was developed. The FSCi algorithm identifies foot-surface contacts in image sequences of gait by quantifying the motion of each foot. The algorithm was validated using standard colour image sequences of walking and running obtained from four unique camera perspectives: output data were compared to three-dimensional motion analysis. The FSCi algorithm identified data for 1243 of 1248 foot-surface contacts; root-mean-square error (RMSE) was 52.2 and 103.4 mm for shod walking and running respectively (all camera perspectives). Findings demonstrated that the FSCi algorithm measured basic, spatio-temporal parameters of walking and running, e.g. step length and step time, without interfering with the activity being observed. Furthermore, analyses were independent of camera view.Video footage obtained from the 2011 ATP World Tour Finals was used to develop a combined player tracking and foot-surface contact identification (PT-FSCi) algorithm. Furthermore, a graphical user interface was developed. The PT-FSCi algorithm was used to analyse twenty match-play tennis rallies: output data were compared to manual digitising. The PT-FSCi algorithm tracked player position and identified data for 832 of 890 foot-surface contacts during match-play tennis. RMSE for player position and foot-surface contacts was 232.9 and 121.9 mm respectively. The calculation of step parameters required manual intervention: this reflected the multi-directional nature of tennis. This represents a limitation to the current algorithm however the segmentation of player movement phases to allow the automatic calculation of step parameters.The analysis of this data indicated that top ranked tennis players can win rallies using movement strategies previously considered to be defensive. Furthermore, step length data indicated that shorter step lengths formed the majority of step strategy. The largest 25% of steps were observed behind the baseline, aligned with deuce and advantage court sidelines. This reflected lunging and turning manoeuvres at lateral extremes of player movement.The single camera system that has resulted from this project will enable the International Tennis Federation to characterise player step and movement strategy during match-play tennis. This will allow a more informed approach to player-surface interaction research. Furthermore, the system has potential to be used for different applications, ranging from sport to surveillance