Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
This work addresses tracking accuracy in complex MOT scenarios, representing an incremental improvement through novel integration of existing techniques.
The paper tackles the problem of Multi-Object Tracking (MOT) by proposing a framework that integrates long-term and short-term cues with switcher-aware classification to handle complex scenes, achieving state-of-the-art results on challenging benchmarks.
In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes. Besides, for better association, we propose switcher-aware classification (SAC), which takes the potential identity-switch causer (switcher) into consideration. Specifically, the proposed framework includes a Single Object Tracking (SOT) sub-net to capture short term cues, a re-identification (ReID) sub-net to extract long term cues and a switcher-aware classifier to make matching decisions using extracted features from the main target and the switcher. Short term cues help to find false negatives, while long term cues avoid critical mistakes when occlusion happens, and the SAC learns to combine multiple cues in an effective way and improves robustness. The method is evaluated on the challenging MOT benchmarks and achieves the state-of-the-art results.