A framework for activity-specific human identification

In this paper we propose a view based approach to recognize humans when engaged in some activity. The width of the outer contour of the binarized silhouette of a walking person is chosen as the image feature. A set of exemplars that occur during an activity cycle is chosen for each individual. Using these exemplars a lower dimensional Frame to Exemplar Distance (FED) vector is generated. A continuous HMM is trained using several such FED vector sequences. This methodology serves to compactly capture structural and dynamic features that are unique to an individual. The statistical nature of the HMM renders overall robustness to representation and recognition. Human identification performance of the proposed scheme is found to be quite good when tested on outdoor video sequences collected using surveillance cameras.