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3 months ago

Multiple Object Tracking Challenge Technical Report for Team MT_IoT

Feng Yan Zhiheng Li Weixin Luo Zequn jie Fan Liang Xiaolin Wei Lin Ma

Multiple Object Tracking Challenge Technical Report for Team MT_IoT

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.

Code Repositories

BingfengYan/DS_OCSORT
Official
pytorch

Benchmarks

BenchmarkMethodologyMetrics
multi-object-tracking-on-dancetrackMT_IOT
AssA: 52.95
DetA: 84.14
HOTA: 66.66
IDF1: 70.6
MOTA: 93.97

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Multiple Object Tracking Challenge Technical Report for Team MT_IoT | Papers | HyperAI