View recognition of human gait sequences in videos

We investigate in this paper the problem of view recognition of human gait sequences in videos. To our knowledge, the problem has not been formally addressed in the literature. Recognizing the views of human gait sequences has a number of potential applications, including visual surveillance and view-invariant human gait recognition. Motivated by the fact that human gait sequences collected from two views with small differences are more easily mis-recognized than those with large differences, we propose a new adaptive discriminant analysis (ADA) method by imposing large penalties on interclass samples with small differences and small penalties on those samples with large differences simultaneously, such that the discriminating power of the extracted features can be boosted for view recognition. Experimental results are presented to demonstrate the efficacy of the proposed approach.