  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.
