SimpleTrack:
Understanding and Rethinking
3D Multi-object Tracking
3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm.
Understanding and Rethinking
3D Multi-object Tracking
3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm.
Despite their progress and usefulness, an in-depth analysis of their strengths and weaknesses is not yet available.
In this paper, they summarize current 3D MOT methods into a unified framework by decomposing them into four constituent parts: pre-processing of detection, association, motion model, and life cycle management.
They then ascribe the failure cases of existing algorithms to each component and investigate them in detail. Based on the analyses, we propose corresponding improvements which lead to a strong yet simple baseline: SimpleTrack.
Comprehensive experimental results on Waymo Open Dataset and nuScenes demonstrate that our final method could achieve new state-of-the-art results with minor modifications.
Furthermore, they take additional steps and rethink whether current benchmarks authentically reflect the ability of algorithms for real-world challenges.
They delve into the details of existing benchmarks and find some intriguing facts. Finally, we analyze the distribution and causes of remaining failures in \name\ and propose future directions for 3D MOT.
🔎📚 Paper: is.gd/QXDp4l
👨🎓 👩🏾🎓 Credit: Ziqi Pang, Zhichao Li, Naiyan Wang
👨🎓 👩🏾🎓 Credit: Ziqi Pang, Zhichao Li, Naiyan Wang
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