This paper presents a preliminary study of an efficient object tracking approach, comparing the performance of two different 3D point cloud sensory sources: LiDAR and stereo cameras, which have significant price differences. In this preliminary work, we focus on single object tracking. We first developed a fast heuristic object detector that utilizes prior information about the environment and target. The resulting target points are subsequently fed into an extended object tracking framework, where the target shape is parameterized using a star-convex hypersurface model. Experimental results show that our object tracking method using a stereo camera achieves performance similar to that of a LiDAR sensor, with a cost difference of more than tenfold.

翻译：本文针对高效目标跟踪方法进行了初步研究，比较了两种具有显著价格差异的3D点云传感源（LiDAR与立体相机）的性能表现。在本初步工作中，我们专注于单目标跟踪任务。首先开发了一种利用环境与目标先验信息的快速启发式目标检测器，所得目标点云随后输入扩展目标跟踪框架，其中目标形状通过星凸超曲面模型进行参数化表征。实验结果表明，采用立体相机的目标跟踪方法在成本相差十余倍的情况下，取得了与LiDAR传感器相近的性能表现。