A Simultaneous Object Detection and Tracking Framework Based on Point Cloud
A Simultaneous Object Detection and Tracking Framework Based on Point Cloud
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一种基于点云的同步目标检测与跟踪框架
DOI：
10.1109/iccrd54409.2022.9730356
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Zhaohui Xiang
中科院分区：
文献类型：
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作者：
Zhaohui Xiang
实现准确有效的多目标检测和跟踪在自动驾驶领域起着非常重要的作用。准确检测行人、汽车、骑自行车的人和其他物体可以提高下游任务的性能，例如避障和路径规划。多目标跟踪是在目标检测结果的基础上，对一段时间内的运动目标进行识别和跟踪。传统的检测跟踪方法不能很好地处理遮挡情况或长时间跟踪问题。本文提出了一种基于时空地图的同时检测和跟踪方法，该方法在长时间内具有上级性能的轨迹连通性和跟踪精度。在检测模块中，我们使用RANSAC算法对每帧激光雷达数据进行平面提取和分割，然后使用欧几里得聚类算法对不同的对象进行聚类和分类。在跟踪模块中，我们提出了一种两帧和多帧可切换的在线跟踪架构。在两帧跟踪中，我们使用卡尔曼滤波器来更新目标位置测量值，以匹配当前帧中的同一目标。对于长时间和多帧跟踪，目标的轨迹直接表示在时空地图上，我们拟合折线在地图中找到最佳的数据关联。在KITTI数据集上的实验结果表明，我们的三维目标检测和跟踪框架是优雅和完整的，跟踪模块是鲁棒的，广泛适用的，成本低。
Achieving accurate and effective detection and multi-object tracking plays a very important role in the field of automatic driving. Accurate detection of pedestrians, cars, cyclists, and other objects can improve the performance of the downstream tasks, such as obstacle avoidance and path planning. Based on the object detection results, multi-object tracking can identify and track moving targets in a period of time. The traditional tracking-by-detection methods can not handle the occlusion situation or long-term tracking problem well. In this paper, we propose a simultaneous detection and tracking method based on spatial temporal map, which has superior performance of trajectory connectivity and tracking accuracy in a long period of time. In the detection module, we use RANSAC algorithm to extract and segment the plane for each frame of lidar data, and then, cluster and classify different objects using the Euclidean Clustering algorithm. In the tracking module, we propose a two-frame and multi-frame switchable architecture for online tracking. In two-frame tracking, we use Kalman filter to update the object location measurements to match the same object in the current frame. For long-term and multi-frame tracking, the trajectories of objects are directly represented on spatial temporal map, and we fit polylines in the map to find the best data association. Experiment results on the KITTI dataset show that our 3D object detection and tracking framework is elegant and complete, the tracking module is robust, widely applicable, and low in cost.