Learning discriminative correlation filters via saliency-aware channel selection for robust visual object tracking

In recent years, discriminative correlation filters (DCF) with deep features have achieved excellent results in visual object tracking tasks. These trackers usually use multi-channel features of the fixed layer of the pre-trained network model to represent the target. However, the multi-channel features contain many interfering channels that are not conducive to object representation, resulting in overfitting and high computational complexity. To solve this problem, we research the correlation between multi-channel deep features and target saliency information and propose a novel DCF tracking method based on saliency-aware and adaptive channel selection. Specifically, we adaptively select the most representative feature channels to represent the target by calculating the energy mean ratio of the saliency-aware region to the search region, reducing the feature dimension and improving the tracking efficiency. Then, according to the feedback, the selected channels are given different weights to further enhance the discrimination of the filter. In addition, an adaptive update strategy is designed to alleviate the model degradation problem according to the fluctuation of feature maps in the recent frames. Finally, we use the alternating direction method of multipliers (ADMM) to optimize the proposed tracker model. Extensive experimental results on five well-known tracking benchmark datasets have verified the superiority of the proposed tracker with many state-of-the-art deep features-based trackers, and the running speed of the algorithm can basically meet the real-time requirements.