Regional deep learning model for visual tracking

Deep learning has been successfully applied to visual tracking due to its powerful feature learning characteristic. However, existing deep learning trackers rely on single observation model and focus on the holistic representation of the tracking object. When occlusion occurs, the trackers suffer from the contaminated features obtained in occluded areas. In this paper, we propose a regional deep learning tracker that observes the target by multiple sub-regions and each region is observed by a deep learning model. In particular, we devise a stable factor, modeled as a hidden variable of the Factorial Hidden Markov Model, to characterize the stability of these sub-models. The stability indicator not only provides a confidence degree for the response score of each model during inference stage, but also determines the online training criteria for each deep learning model. This online training strategy enables the tracker to achieve more accurate local features compared with those fixed training trackers. In addition, to improve the computational efficiency, we exploit the structurized response property of the customized deep learning model to approximate the final tracking results by the weighted Gaussian Mixture Model under the particle filter framework. Qualitative and quantitative evaluations on the recent public benchmark dataset show that our approach outperforms most state-of-the-art trackers.