UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation
Kefu Yi, Kai Luo, Xiaolei Luo, Jiangui Huang, Hao Wu, Rongdong Hu, Wei, Hao

TL;DR
UCMCTrack introduces a robust, motion-based multi-object tracking method that maintains consistent camera motion compensation throughout sequences, achieving state-of-the-art results without relying on appearance cues.
Contribution
It proposes a novel motion model that applies uniform camera motion compensation across sequences and introduces Mapped Mahalanobis Distance for improved tracking accuracy.
Findings
Achieves state-of-the-art performance on MOT17, MOT20, DanceTrack, and KITTI datasets.
Operates solely on motion cues, reducing computational complexity.
Outperforms traditional methods requiring appearance cues or frame-by-frame compensation.
Abstract
Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Vision and Imaging · Image Enhancement Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
