Enhancing Point Cloud Tracking With Spatio-Temporal Triangle Optimization

Abstract

Single object tracking (SOT) is a critical task in computer vision, playing a substantial role in fields such as autonomous driving. It represents a spatio-temporal learning challenge, aimed at tracking a designated target, indicated by a bounding box (BBox), in video sequences of 3-D point cloud data. Recent developments in the target-motion-estimation (TME) paradigm for SOT have achieved significant performance improvements. This paradigm involves predicting the relative target motion (RTM), which captures the shifts in the target’s center and orientation between consecutive frames. However, existing TME-based methods utilize separate and complex optimizations to regress the RTM, with the RTM’s coordinates shift and rotation angle representing motion properties. Furthermore, RTM fails to offer a comprehensive representation of target motion, such as the motion patterns of objects with varying sizes and movement ranges. Additionally, the current paradigm for multiframe tracking is sensitive to noise and distractors, due to the unrobust proposal selection method based on point counting. To address these limitations, a spatio-temporal triangle (STT) optimization method is proposed. All optimization steps from existing TME-based methods are integrated into a STT optimization, simplifying the process and improving integration. Diagonals (BBox corners) are used to denote the BBox parameters, allowing the RTM to be represented as a spatio-temporal area swept by diagonals, thus providing a consistent motion measurement and comprehensive motion patterns. A novel proposal selection method is introduced, selecting proposals based on the highest intersection over union (IoU) with the ground truth (GT), ensuring more robust tracking in scenarios with multiple distractors. Extensive experiments demonstrate that the proposed STT optimization method significantly enhances tracking performance, resulting in improvements of $\uparrow 1.5$ / $\uparrow 2.2$ on the KITTI dataset and $\uparrow 2.22$ / $\uparrow 2.45$ on nuScenes for the two-frame model, and $\uparrow 2.8$ / $\uparrow 2.4$ on KITTI and $\uparrow 3.46$ / $\uparrow 4.47$ on nuScenes for the multiframe model, achieving state-of-the-art results on both datasets.