Visual Object Tracking Algorithm Based on Hard Negative Mining and Adaptive Temporal Regularization

To alleviate the severe performance degradation of visual object tracking algorithms in complex situations, a visual object tracking algorithm based on hard negative mining and adaptive temporal regularization is proposed. Firstly, hard negative samples are deeply mined based on the Staple algorithm to train the correlation filter, which improves the anti-jamming ability of the tracking algorithm. Secondly, the adaptive temporal regularization term is added, and the coefficient of temporal regularization term and the model updating strategy are adaptively determined by the response map variation, which improves the identification ability of the tracking algorithm. The experimental results on datasets OTB-2015, TC-128 and UAV123 show that the proposed algorithm can effectively deal with the tracking performance degradation in complex situations, and its running speed is more than 30 frames per second, which meets the real-time requirement. Its comprehensive performance is better than the comparative algorithms.