Multiple Object Tracking with Dynamic Adaptive Object Motion Estimation

Abstract

One indicator for evaluating autonomous vehicles’ capability is the accuracy of perceiving the surrounding environment. As an essential part of perception, MOT (multi-object tracking) algorithms provide vital guarantees for safe driving. However, many MOT algorithms based on the motion model only consider the information from previous frame when predicting the motion state of objects without taking into account the long-term motion state. Moreover, their motion model generally uses constant speed or acceleration models, which may cause tracking loss when the object suddenly changes its motion or is occluded by other objects. In this paper, we propose DA-MOT (Multiple Object Tracking with Dynamic Adaptive Object Motion Estimation), which utilizes information from lidar and camera sensors to calculate objects’ dynamic and static states under different sensor information. We modify the KF motion model parameters based on the object’s motion for better tracking performance. Furthermore, we design a re-association mechanism to re-assign IDs for inaccurate associations. We conducted experiments on the KITTI dataset, and the results show a significant improvement in accuracy. DA-MOT algorithm has about 1.5% improvement in MOTA metrics compared to other MOT algorithms in scenes with large changes in object state and can run about 1000 fps on the Intel Core Intel® Xeon(R) Gold 5217 CPU.