arXiv:2212.03586
공식 코드 · Papers with Code
Multiple Object Tracking Challenge Technical Report for Team MT_IoT
원문 초록
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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ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
Asynchronous Multi-Object Tracking with an Event Camera
FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking
Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking
The Mean of Multi-Object Trajectories
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