A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification
Re-identification refers to the problem of establishing correspondence among various observations of the same subject viewed at different time instances in different camera positions. We propose a hierarchical approach for re-identifying a subject by combining gait with phase of motion and a spatiotemporal model. The fundamental nature of the gait biometric of being amenable to capturing from a distance even at low resolution without active co-operation of subjects, has motivated us to use it for re-identification. We use two features related to a subjects motion dynamics, one is his exit/entry phase of motion and the other is his gait signature. An additional third feature is obtained from the spatiotemporal model of the camera network which is learnt during the training phase in the form of a multivariate probability density of spacetime variables (entry/exit location, exit velocity, and inter-camera travel time) using kernel density estimation. Once all these three features have been computed, correspondences are established by dynamic programing based maximum likelihood (ML) estimation. The performance of our method has been evaluated on a real data set featuring a two-camera and a three-camera network in a hallway monitoring situation. The proposed approach shows promising results on both the data sets.
gait phase of motion spatiotemporal model re-identification dynamic programing pose energy image