Facial Feature-Integrated Inter-Camera Human Tracking

Abstract

This paper presents a new scheme to perform inter-camera human tracking in a surveillance camera network with high resolution cameras by taking advantage of all possible collected visual information. The proposed approach utilizes the tracked trajectory information of pedestrians within a camera to get accurate face positions and poses. To solve varied face pose problem under different cameras, we frontalize random posed face with a generic 2D-to-3D mapping matrix between facial feature points. Texture-based face descriptor is then exploited to extract useful features from facial components and combined with pose-invariant appearance feature, which models dominant color components in two partitioned body regions as GMM. The proposed algorithm shows promising performance by evaluating on the public benchmark Dana36 dataset.