Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking

• URL: http://arxiv.org/abs/2403.15831v1
• Date: Sat, 23 Mar 2024 13:15:44 GMT
• Title: Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking
• Authors: Shaoyu Sun, Chunyang Wang, Xuelian Liu, Chunhao Shi, Yueyang Ding, Guan Xi,
• Abstract summary: 3 single object tracking within LIDAR point is pivotal task in computer vision. Existing methods, which depend solely on appearance matching via networks or utilize information from successive frames, encounter significant challenges. We design an innovative cross-frame bi-temporal motion tracker, named STMD-Tracker, to mitigate these challenges.
• Score: 2.487142846438629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D single object tracking within LIDAR point clouds is a pivotal task in computer vision, with profound implications for autonomous driving and robotics. However, existing methods, which depend solely on appearance matching via Siamese networks or utilize motion information from successive frames, encounter significant challenges. Issues such as similar objects nearby or occlusions can result in tracker drift. To mitigate these challenges, we design an innovative spatio-temporal bi-directional cross-frame distractor filtering tracker, named STMD-Tracker. Our first step involves the creation of a 4D multi-frame spatio-temporal graph convolution backbone. This design separates KNN graph spatial embedding and incorporates 1D temporal convolution, effectively capturing temporal fluctuations and spatio-temporal information. Subsequently, we devise a novel bi-directional cross-frame memory procedure. This integrates future and synthetic past frame memory to enhance the current memory, thereby improving the accuracy of iteration-based tracking. This iterative memory update mechanism allows our tracker to dynamically compensate for information in the current frame, effectively reducing tracker drift. Lastly, we construct spatially reliable Gaussian masks on the fused features to eliminate distractor points. This is further supplemented by an object-aware sampling strategy, which bolsters the efficiency and precision of object localization, thereby reducing tracking errors caused by distractors. Our extensive experiments on KITTI, NuScenes and Waymo datasets demonstrate that our approach significantly surpasses the current state-of-the-art methods.

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