Compensation Tracker: Data Association Method for Lost Object.

TL;DR: Experiments show that after using the compensation tracker designed in this paper, evaluation indicators have improved in varying degrees on MOT Challenge data sets and the proposed method can effectively improve the tracking performance of the model.

Abstract: At present, the main research direction of multi-object tracking framework is detection-based tracking method. Although the detection-based tracking model can achieve good results, it is very dependent on the performance of the detector. The tracking results will be affected to a certain extent when the detector has the behaviors of omission and error detection. Therefore, in order to solve the problem of missing detection, this paper designs a compensation tracker based on Kalman filter and forecast correction. Experiments show that after using the compensation tracker designed in this paper, evaluation indicators have improved in varying degrees on MOT Challenge data sets. In particular, the multi-object tracking accuracy reached 66% in the 2020 datasets of dense scenarios. This shows that the proposed method can effectively improve the tracking performance of the model.