Combining short-term and long-term memory for robust visual tracking

Abstract

In visual object tracking, addressing challenges such as target appearance deformation and occlusion has attracted increasing attention. To this end, this paper proposes CSLMTrack, a multiple memory tracking model that more comprehensively reflects the human memory mechanism. It contains short-term and long-term memory modules, as well as a novel feed-forward network TFFN for temporal information aggregation. A dynamic memory update strategy including memory, information transfer, recall, and forgetting processes is also designed, which can effectively avoid memory explosion while integrating memory elements into the tracking network. Extensive experiments conducted on multiple challenging benchmarks demonstrate that CSLMTrack achieves impressive performance, reaching SOTA-level performance compared to state-of-the-art trackers.