  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.
