Human re-identification in multi-camera systems

We propose a human re-identification algorithm for multi-camera surveillance environment where a unique signature of an individual is learned and tracked in a scene. The video feed from each camera is processed using a motion detector to get locations of all individuals. To compute the human signature, we propose a combination of different descriptors on the detected body such as the Local Binary Pattern Histogram (LBPH) for the local texture and a HSV color-space based descriptor for the color representation. For each camera, a signature computed by these descriptors is assigned to the corresponding individual along with their direction in the scene. Knowledge of the persons direction allows us to make separate identifiers for the front, back, and sides. These signatures are then used to identify individuals as they walk across different areas monitored by different cameras. The challenges involved are the variation of illumination conditions and scale across the cameras. We test our algorithm on a dataset captured with 3 Axis cameras arranged in the UD Vision Lab as well as a subset of the SAIVT dataset and provide results which illustrate the consistency of the labels as well as precision/accuracy scores.