A motion-based, correspondence-free technique for human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of im- ages of the person, and that gait recognition can be achieved via standard pattern classification of these plots. We use background modelling to track the person for a number of frames and extract a sequence of segmented images of the person. The self-similarity plot is computed via correlation of each pair of images in this se- quence. For recognition, the method applies Principal Component Analysis to reduce the dimensionality of the plots, then uses the k-nearest neighbor rule in this reduced space to classify an un- known person. This method is robust to tracking and segmentation errors, and to variation in clothing and background. It is also in- variant to small changes in camera viewpoint and walking speed. The method is tested on outdoor sequences of 44 people with 4 se- quences of each taken on two different days, and achieves a clas- sification rate of 77%. It is also tested on indoor sequences of 7 people walking on a treadmill, taken from 8 different viewpoints and on 7 different days. A classification rate of 78% is obtained for near-fronto-parallel views, and 65% on average over all view.<\/p>\n","protected":false},"excerpt":{"rendered":"
A motion-based, correspondence-free technique for human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of im- ages of the person, and that gait recognition can be achieved via standard pattern classification 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