Fully Unsupervised Learning of Camera Link Models for Tracking Humans Across Nonoverlapping Cameras

Abstract

A multiple-camera tracking system that tracks humans across cameras with nonoverlapping views is proposed in this paper. The systematically estimated camera link model, including transition time distribution, brightness transfer function, region mapping matrix, region matching weights, and feature fusion weights, is utilized to facilitate consistently labeling the tracked humans. The system is divided into two stages: in the training stage, based on an unsupervised scheme, we formulate the estimation of the camera link model as an optimization problem, in which temporal features, holistic color features, region color features, and region texture features are jointly considered. The deterministic annealing is applied to effectively search the optimal model solutions. The unsupervised learning scheme tolerates the presence of outliers in the training data well. In the testing stage, the systematic integration of multiple cues from the above features enables us to perform an effective reidentification. The camera link model can be continuously updated during tracking in the testing stage to adapt the changes of the environment. Several simulations and comparative studies demonstrate the superiority of our proposed estimation method to the others. Moreover, the complete system has been tested in a small-scale real-world camera network scenario.