Appearance-based Human Gallery Construction from Video

An approach for constructing a dynamic gallery of people observed in a video stream is described. We consider two scenarios that require determining the number and identity of participants: outdoor surveillance and meeting rooms. In these applications face identification is typically not feasible due to the low resolution across the face. The proposed approach automatically computes an appearance model based on the clothing of people and employs this model in constructing and matching the gallery of participants. The appearance model uses color/path-length profile and a robust distance measure based on Kernel Density Estimation (KDE) and Kullback-Leibler (KL) distance, to evaluate similarity between people and add models to the gallery. A one-to-one constraint is enforced to correctly match instances to models at each frame. In the meeting room scenario we exploit the fact that the relative locations of subjects are likely to remain unchanged for the whole sequence.