A Content-Aware Correlation Filter with Multi-Feature Fusion for RGB-T Tracking

In challenging situations, such as low illumination, rain, and background clutter, the stability of the thermal infrared (TIR) spectrum can help red, green, blue (RGB) visible spectrum to improve tracking performance. However, the high-level image information and the modality-specific features have not been sufficiently studied. The proposed correlation filter uses the fused saliency content map to improve filter training and extracts different features of modalities. The fused content map is introduced into the spatial regularization term of correlation filter to highlight the training samples in the content region. Furthermore, the fused content map can avoid the incompleteness of the content region caused by challenging situations. Additionally, different features are extracted according to the modality characteristics and are fused by the designed response-level fusion strategy. The alternating direction method of multipliers (ADMM) algorithm is used to solve the tracker training efficiently. Experiments on the large-scale benchmark datasets show the effectiveness of the proposed tracker compared to the state-of-the-art traditional trackers and the deep learning based trackers.