In recent years, one-stage multi-object tracking (MOT) methods, which jointly learn detection and identification in a single network, have attracted extensive attention, due to their efficiency. However, the negative transfer effects caused by the two conflicting objectives of detection and identification have rarely been explored. In this paper, we propose a Detection-Identification Balancing Margin (DIM) loss for minimizing the adverse effects caused by these two different objectives. The proposed DIM loss consists of Detection Margin (DM) loss and Identification Margin (IM) loss. DM loss forces features that are farther from the center of the foreground features than the defined margin due to identification learning to be converged to ensure accurate detection. IM loss enables the various feature representations that are essential for identification by intentionally spreading features that become overly clustered due to detection learning. The proposed DIM loss demonstrates competitive and balanced performance for MOT by providing a positive transfer for features that had a strong negative impact on detection and identification, respectively. (HOTA 61.5, MOTA 75.3, IDF1 75.6 on MOT16, and real-time rates of 25.9 fps were achieved).
|Title of host publication||2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2022|
|Event||29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France|
Duration: 2022 Oct 16 → 2022 Oct 19
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Conference||29th IEEE International Conference on Image Processing, ICIP 2022|
|Period||22/10/16 → 22/10/19|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by the Institute of Information communications Technology Planning Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2021-0-00172, The development of human Re-identification and masked face recognition based on CCTV camera).
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing