Reference Set Based Appearance Model for Tracking Across Non-overlapping Cameras

Multi-target tracking in non-overlapping cameras is challenging due to the vast appearance change of the targets across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Therefore, direct track association is difficult and prone to error. In most previous methods the appearance similarity is computed either using color histograms directly or based on pre-trained Brightness Transfer Function (BTF) that maps color between cameras. In this paper, we propose a novel reference set based appearance model to improve multi-target tracking in a network of non-overlapping video cameras. Unlike previous work, a reference set is constructed for a pair of cameras, containing targets appearing in both camera views. For track association, instead of comparing the appearance of two targets in different camera views directly, they are compared to the reference set. The reference set acts as a basis to represent a target by measuring the similarity between the target and each of the individuals in the reference set. Besides color histograms, other soft-biometric features are also integrated into the feature representation of a target. The effectiveness of the proposed method over the baseline models on challenging real-world multi-camera video data is validated by the experiments.