Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking
This work addresses tracking failures in real-time multi-object tracking systems, offering an incremental improvement for applications like surveillance and autonomous driving.
The paper tackles the problem of lost objects in multi-object tracking due to detector failures by proposing a compensation tracker that reprocesses lost objects, improving MOTA to 66% and IDF1 to 67% on the MOT2020 dataset.
Tracking by detection paradigm is one of the most popular object tracking methods. However, it is very dependent on the performance of the detector. When the detector has a behavior of missing detection, the tracking result will be directly affected. In this paper, we analyze the phenomenon of the lost tracking object in real-time tracking model on MOT2020 dataset. Based on simple and traditional methods, we propose a compensation tracker to further alleviate the lost tracking problem caused by missing detection. It consists of a motion compensation module and an object selection module. The proposed method not only can re-track missing tracking objects from lost objects, but also does not require additional networks so as to maintain speed-accuracy trade-off of the real-time model. Our method only needs to be embedded into the tracker to work without re-training the network. Experiments show that the compensation tracker can efficaciously improve the performance of the model and reduce identity switches. With limited costs, the compensation tracker successfully enhances the baseline tracking performance by a large margin and reaches 66% of MOTA and 67% of IDF1 on MOT2020 dataset.