Learning Historical Status Prompt for Accurate and Robust Visual Tracking

Most trackers perform template and search region similarity matching to find the most similar object to the template during tracking. However, they struggle to make prediction when the target appearance changes due to the limited historical information introduced by roughly cropping the current search region based on the predicted result of previous frame. In this paper, we identify that the central impediment to improving the performance of existing trackers is the incapacity to integrate abundant and effective historical information. To address this issue, we propose a Historical Information Prompter (HIP) to enhance the provision of historical information. We also build HIPTrack upon HIP module. HIP is a plug-and-play module that make full use of search region features to introduce historical appearance information. It also incorporates historical position information by constructing refined mask of the target. HIP is a lightweight module to generate historical information prompts. By integrating historical information prompts, HIPTrack significantly enhances the tracking performance without the need to retrain the backbone. Experimental results demonstrate that our method outperforms all state-of-the-art approaches on LaSOT, LaSOT ext, GOT10k and NfS. Futhermore, HIP module exhibits strong generality and can be seamlessly integrated into trackers to improve tracking performance. The source code and models will be released for further research.

SparseTT: Visual Tracking with Sparse Transformers

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT.