Institute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
Department of Automotive Technology, Erbil Technology College, Erbil Polytechnic University, Erbil 44001, Iraq.
Sensors (Basel). 2023 Mar 23;23(7):3390. doi: 10.3390/s23073390.
Multi-object tracking (MOT) is a prominent and important study in point cloud processing and computer vision. The main objective of MOT is to predict full tracklets of several objects in point cloud. Occlusion and similar objects are two common problems that reduce the algorithm's performance throughout the tracking phase. The tracking performance of current MOT techniques, which adopt the 'tracking-by-detection' paradigm, is degrading, as evidenced by increasing numbers of identification (ID) switch and tracking drifts because it is difficult to perfectly predict the location of objects in complex scenes that are unable to track. Since the occluded object may have been visible in former frames, we manipulated the speed and location position of the object in the previous frames in order to guess where the occluded object might have been. In this paper, we employed a unique intersection over union (IoU) method in three-dimension (3D) planes, namely a distance IoU non-maximum suppression (DIoU-NMS) to accurately detect objects, and consequently we use 3D-DIoU for an object association process in order to increase tracking robustness and speed. By using a hybrid 3D DIoU-NMS and 3D-DIoU method, the tracking speed improved significantly. Experimental findings on the Waymo Open Dataset and nuScenes dataset, demonstrate that our multistage data association and tracking technique has clear benefits over previously developed algorithms in terms of tracking accuracy. In comparison with other 3D MOT tracking methods, our proposed approach demonstrates significant enhancement in tracking performances.
多目标跟踪 (MOT) 是点云处理和计算机视觉领域中一项重要而突出的研究。MOT 的主要目标是预测点云中的几个物体的完整轨迹。遮挡和相似物体是两个常见的问题，会降低跟踪阶段算法的性能。当前采用“跟踪即检测”范式的 MOT 技术的跟踪性能正在下降，因为 ID 切换和跟踪漂移的数量不断增加，这是因为很难在无法跟踪的复杂场景中完美预测物体的位置。由于被遮挡的物体在前几帧中可能可见，因此我们在前几帧中操纵物体的速度和位置，以猜测被遮挡的物体可能在哪里。在本文中，我们在三维 (3D) 平面中使用了一种独特的交并比 (IoU) 方法，即距离 IoU 非极大值抑制 (DIoU-NMS)，以准确检测物体，然后我们使用 3D-DIoU 进行物体关联过程，以提高跟踪的鲁棒性和速度。通过使用混合 3D DIoU-NMS 和 3D-DIoU 方法，跟踪速度显著提高。在 Waymo Open Dataset 和 nuScenes 数据集上的实验结果表明，与之前开发的算法相比，我们的多阶段数据关联和跟踪技术在跟踪精度方面具有明显的优势。与其他 3D MOT 跟踪方法相比，我们提出的方法在跟踪性能方面有显著的提高。
