Persistent multi-object tracking (MOT) allows autonomous vehicles to navigate safely in highly dynamic environments. One of the well-known challenges in MOT is object occlusion when an object becomes unobservant for subsequent frames. The current MOT methods store objects information, like objects' trajectory, in internal memory to recover the objects after occlusions. However, they retain short-term memory to save computational time and avoid slowing down the MOT method. As a result, they lose track of objects in some occlusion scenarios, particularly long ones. In this paper, we propose DFR-FastMOT, a light MOT method that uses data from a camera and LiDAR sensors and relies on an algebraic formulation for object association and fusion. The formulation boosts the computational time and permits long-term memory that tackles more occlusion scenarios. Our method shows outstanding tracking performance over recent learning and non-learning benchmarks with about 3% and 4% margin in MOTA, respectively. Also, we conduct extensive experiments that simulate occlusion phenomena by employing detectors with various distortion levels. The proposed solution enables superior performance under various distortion levels in detection over current state-of-art methods. Our framework processes about 7,763 frames in 1.48 seconds, which is seven times faster than recent benchmarks. The framework will be available at https://github.com/MohamedNagyMostafa/DFR-FastMOT.

翻译：持久性多目标跟踪（MOT）使自动驾驶车辆能够在高度动态的环境中安全导航。MOT中一个众所周知的挑战是目标遮挡，即目标在后续帧中变得不可观测。当前MOT方法将目标信息（如目标轨迹）存储在内部存储器中，以便在遮挡后恢复目标。然而，它们仅保留短期记忆以节省计算时间并避免降低MOT方法的速度。因此，在某些遮挡场景（尤其是长时间遮挡）中，它们会丢失对目标的跟踪。在本文中，我们提出DFR-FastMOT，一种轻量级MOT方法，它利用来自摄像头和激光雷达传感器的数据，并基于目标关联与融合的代数公式。该公式提升了计算速度，并允许使用长期记忆以应对更多遮挡场景。我们的方法在近期学习和非学习基准测试中均表现出卓越的跟踪性能，MOTA分别高出约3%和4%。此外，我们通过采用不同失真级别的检测器，进行了广泛实验以模拟遮挡现象。所提出的解决方案在检测的各种失真级别下都优于现有最先进方法。我们的框架处理约7,763帧仅需1.48秒，比近期基准测试快七倍。该框架将在https://github.com/MohamedNagyMostafa/DFR-FastMOT上提供。