Adaptive Colour-Depth Aware Attention for RGB-D Object Tracking

Recent advances in RGB-D tracking have been driven by the synergistic combination of high-performing RGB-only trackers and auxiliary depth information. However, most existing methods rely on visual feature descriptors to extract depth features, which are then fused with vision features. This pipeline may lead to performance degradation due to the incongruence between the RGB and depth modalities. In this letter, we propose an efficient and effective transformer-based framework, that explicitly models colour and depth information for RGB-D tracking. Specifically, we first statistically code the colour and depth information of the foreground and background for the template. Then, the spatial attention maps of the search region are obtained using these colour-depth statistical models, enhancing the visual features of the search region for improved object localisation accuracy. The comprehensive experimental results obtained on multiple benchmarks demonstrate the effectiveness and merits of the proposed approach in explicit colour-depth coding for RGB-D tracking. The code and the models are publicly accessible at
https://github.com/xuefeng-zhu5/CDAAT
.