Siamese hierarchical feature fusion transformer for efficient tracking - Dai, Jiahai; Fu, Yunhao; Wang, Songxin; Chang, Yuchun (Frontiers in Neurorobotics, 2022)
Object tracking is a fundamental task in computer vision. Recent years, most of the tracking algorithms are based on deep networks. Trackers with deeper backbones are computationally expensive and can hardly meet the real-time requirements on edge platforms. Lightweight networks are widely used to tackle this issue, but the features extracted by a lightweight backbone are inadequate for discriminating the object from the background in complex scenarios, especially for small objects tracking task. In this paper, we adopted a lightweight backbone and extracted features from multiple levels. A hierarchical feature fusion transformer (HFFT) was designed to mine the interdependencies of multi-level features in a novel model—SiamHFFT. Therefore, our tracker can exploit comprehensive feature representations in an end-to-end manner, and the proposed model is capable of handling small target tracking in complex scenarios on a CPU at a rate of 29 FPS. Comprehensive experimental results on UAV123, UAV123@10fps, LaSOT, VOT2020, and GOT-10k benchmarks with multiple trackers demonstrate the effectiveness and efficiency of SiamHFFT. In particular, our SiamHFFT achieves good performance both in accuracy and speed, which has practical implications in terms of improving small object tracking performance in the real world.