SiamMo: Siamese Motion-Centric 3D Object Tracking

• URL: http://arxiv.org/abs/2408.01688v2
• Date: Mon, 9 Sep 2024 05:24:27 GMT
• Title: SiamMo: Siamese Motion-Centric 3D Object Tracking
• Authors: Yuxiang Yang, Yingqi Deng, Jing Zhang, Hongjie Gu, Zhekang Dong,
• Abstract summary: We introduce SiamMo, a novel and simple motion-centric tracking approach. Unlike the traditional single-stream architecture, we introduce Siamese feature extraction for motion-centric tracking. SiamMo sets a new record on the KITTI tracking benchmark with 90.1% precision while maintaining a high inference speed of 108 FPS.
• Score: 12.68616041331354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current 3D single object tracking methods primarily rely on the Siamese matching-based paradigm, which struggles with textureless and incomplete LiDAR point clouds. Conversely, the motion-centric paradigm avoids appearance matching, thus overcoming these issues. However, its complex multi-stage pipeline and the limited temporal modeling capability of a single-stream architecture constrain its potential. In this paper, we introduce SiamMo, a novel and simple Siamese motion-centric tracking approach. Unlike the traditional single-stream architecture, we employ Siamese feature extraction for motion-centric tracking. This decouples feature extraction from temporal fusion, significantly enhancing tracking performance. Additionally, we design a Spatio-Temporal Feature Aggregation module to integrate Siamese features at multiple scales, capturing motion information effectively. We also introduce a Box-aware Feature Encoding module to encode object size priors into motion estimation. SiamMo is a purely motion-centric tracker that eliminates the need for additional processes like segmentation and box refinement. Without whistles and bells, SiamMo not only surpasses state-of-the-art methods across multiple benchmarks but also demonstrates exceptional robustness in challenging scenarios. SiamMo sets a new record on the KITTI tracking benchmark with 90.1\% precision while maintaining a high inference speed of 108 FPS. The code will be released at https://github.com/HDU-VRLab/SiamMo.

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