Enhancing Robustness of Multi-Object Trackers With Temporal Feature Mix

Despite its recent advancements, multi-object tracking (MOT), one of the major research areas in video technology, still faces various challenges, including severe occlusion and diversity of tracking targets. In this paper, we introduce a novel strategy, Temporal Feature Mix (TFM), that can improve the overall robustness of multi-object trackers in diverse scenarios. More specifically, our approach simulates new and challenging scenes that can train networks to better localize the targets by blending high-level features from temporally adjacent frames with the insights that the high-level features are mainly activated on salient targets and the targets on the adjacent frames are nearly located. Therefore, our TFM can offer novel and diversified training experiences to the networks, achieved through the intensive augmentation of the high-level features of each target. As a result, our approach demonstrates notable performance improvement with three major MOT benchmarks and a newly constructed corruption dataset for MOT, underscoring its potential to enhance the robustness of MOT systems in real-world scenarios. Every related source code is released at https://github.com/kamkyu94/Temporal_Feature_Mix.