Online fast deep learning tracker based on deep sparse neural networks

Abstract

Deep learning can explore robust and powerful feature representations from data and has gained significant attention in visual tracking tasks. However, due to its high computational complexity and time-consuming training process, the most existing deep learning based trackers require an offline pre-training process on a large scale dataset, and have low tracking speeds. Therefore, aiming at these difficulties of the deep learning based trackers, we propose an online deep learning tracker based on Sparse Auto-Encoders (SAE) and Rectifier Linear Unit (ReLU). Combined ReLU with SAE, the deep neural networks (DNNs) obtain the sparsity similar to the DNNs with offline pre-training. The inherent sparsity make the deep model get rid of the complex pre-training process and can be used for online-only tracking well. Meanwhile, the technique of data augmentation is employed in the single positive sample to balance the quantities of positive and negative samples, which improve the stability of the model to some extent. Finally, in order to overcome the problem of randomness and drift of particle filter, we adopt a local dense sampling searching method to generate a local confidence map to locate the target’s position. Moreover, several corresponding update strategies are proposed to improve the robustness of the proposed tracker. Extensive experimental results show the effectiveness and robustness of the proposed tracker in challenging environment against state-of-the-art methods. Not only the proposed tracker leaves out the complicated and time-consuming pre-training process efficiently, but achieves an online fast and robust tracking.