Multiple Object Tracking Challenge Technical Report for Team MT_IoT

• URL: http://arxiv.org/abs/2212.03586v1
• Date: Wed, 7 Dec 2022 12:00:51 GMT
• Title: Multiple Object Tracking Challenge Technical Report for Team MT_IoT
• Authors: Feng Yan, Zhiheng Li, Weixin Luo, Zequn jie, Fan Liang, Xiaolin Wei, Lin Ma
• Abstract summary: 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. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.
• Score: 41.88133094982688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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