SiamMo: Siamese Motion-Centric 3D Object Tracking
Yuxiang Yang, Yingqi Deng, Jing Zhang, Hongjie Gu, Zhekang Dong

TL;DR
SiamMo is a simple, motion-centric 3D object tracking method that improves accuracy and robustness by decoupling feature extraction from temporal fusion and integrating multi-scale motion features, outperforming state-of-the-art methods.
Contribution
Introducing SiamMo, a novel Siamese motion-centric tracking approach with a multi-scale feature aggregation and size-aware encoding, enhancing 3D tracking performance and robustness.
Findings
Achieves 90.1% precision on KITTI benchmark
Surpasses state-of-the-art tracking methods
Operates at 108 FPS with high robustness
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Advanced Image and Video Retrieval Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
