ConfTrack: Kalman Filter-based Multi-Person Tracking by Utilizing Confidence Score of Detection Box
Hyeonchul Jung, Seokjun Kang, T.K. Kim, HyeongKi Kim
Abstract
Kalman filter-based tracking-by-detection (KFTBD) trackers are effective methods for solving multi-person tracking tasks. However, in crowd circumstances, noisy detection results (bounding boxes with low-confidence scores) can cause ID switch and tracking failure of trackers since these trackers utilize the detector’s output directly. In this paper, to solve the problem, we suggest a novel tracker called ConfTrack based on a KFTBD tracker. Compared with conventional KFTBD trackers, ConfTrack consists of novel algorithms, including low-confidence object penalization and cascading algorithms for effectively dealing with noisy detector outputs. ConfTrack is tested on diverse domains of datasets such as the MOT17, MOT20, DanceTrack, and HiEve datasets. ConfTrack has proved its robustness in crowd circumstances by achieving the highest score at HOTA and IDF1 metrics in the MOT20 dataset.