Towards Generalizable Multi-Object Tracking
원문 초록
Multi-Object Tracking MOT encompasses various tracking scenarios, each characterized by unique traits. Effective trackers should demonstrate a high degree of generalizability across diverse scenarios. However, existing trackers struggle to accommodate all aspects or necessitate hypothesis and experimentation to customize the association information motion and or appearance for a given scenario, leading to narrowly tailored solutions with limited generalizability. In this paper, we investigate the factors that influence trackers generalization to different scenarios and concretize them into a set of tracking scenario attributes to guide the design of more generalizable trackers. Furthermore, we propose a point-wise to instance-wise relation framework for MOT, i.e., GeneralTrack, which can generalize across diverse scenarios while eliminating the need to balance motion and appearance. Thanks to its superior generalizability, our proposed GeneralTrack achieves state-of-the-art performance on multiple benchmarks and demonstrates the potential for domain generalization. https://github.com/qinzheng2000/GeneralTrack.git
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ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
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
LMOT: Efficient Light-Weight Detection and Tracking in Crowds
RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
토론
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