Online object tracking based on CNN with metropolis-hasting re-sampling

Tracking-by-learning strategies have been effiective in solv-ing many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learn-ing based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overffitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable train-ing data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-Art trackers on challenging benchmark video sequences.