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:持久性多球跟踪(MOT) 使自动飞行器能够在高度动态的环境中安全航行。 MOT 中众所周知的挑战之一是当一个对象变得不为后续框架所观察时, 对象封闭。 目前的MOT 方法将物体信息( 如物体轨迹) 储存在内部记忆中, 以便在封闭后恢复物体。 但是, 它们保留短期内存, 以节省计算时间, 避免减缓MOT 方法。 结果, 在某些封闭情景中, 特别是长的情景中, 它们丢失了对象的轨道。 在本文中, 我们提议DFR- FastMOT, 这是一种使用相机和LIDAR传感器数据并依靠代数组合和聚变配方法的轻点隐蔽方法。 配方能提升计算时间, 并允许长期内存, 解决更多隐蔽情景。 我们的方法显示最近学习和非学习基准的出色跟踪业绩, 其中分别占3%和4%的差值。 另外, 我们进行广泛的实验, 通过使用不同扭曲程度的检测器和LIDAR 传感器, 3, 提议的解决办法使得我们在各种变相框架下, 7 。</s>