Multiple Object Tracking Challenge Technical Report for Team MT_IoT
This work addresses tracking challenges in complex scenes for computer vision applications, but it appears incremental as it builds on existing two-stage approaches.
The paper tackles multiple-object tracking in complex environments by proposing a two-stage method with an improved human detector and location-wise matching matrix, achieving 66.672 HOTA and 93.971 MOTA on the DanceTrack dataset.
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.