Person Re-identification Using Spatial Covariance Regions of Human Body Parts

Slawomir Bak (1) , Etienne Corvee (1) , François Bremond (1) , Monique Thonnat (1)

1 PULSAR - Perception Understanding Learning Systems for Activity Recognition

In many surveillance systems there is a requirement to determine whether a given person of interest has already been observed over a network of cameras. This is the person re-identification problem. The human appearance obtained in one camera is usually different from the ones obtained in another camera. In order to re-identify people the human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model based on spatial covariance regions extracted from human body parts. The new spatial pyramid scheme is applied to capture the correlation between human body parts in order to obtain a discriminative human signature. The human body parts are automatically detected using Histograms of Oriented Gradients (HOG). The method is evaluated using benchmark video sequences from i-LIDS Multiple-Camera Tracking Scenario data set. The re-identification performance is presented using the cumulative matching characteristic (CMC) curve. Finally, we show that the proposed approach outperforms state of the art methods.

Domaines: Informatique

Slawomir Bak, Etienne Corvee, François Bremond, Monique Thonnat. Person Re-identification Using Spatial Covariance Regions of Human Body Parts. 7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS - 2010, Aug 2010, Boston, United States. ⟨inria-00496116⟩