Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor

Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high detection requirements and the slow target-extraction problem of a common camera, this article proposes to utilize neuromorphic vision sensor (DVS) for detecting the moving objects and estimating their movement states. For a better detection work, the DVS image's noise points are filtered and the CTRV kinematics model is built in advance. In order to distinguish the overlapped or nearby bodies and get the accurate clustering number, this article proposes a 3-D improved $K$ -means method. As the clustering centers can be influenced by the movement easily, the moving objects' clustering centers appear unstable, so the movement estimation also fluctuates. In order to obtain stable movement estimation, this article proposes a strong tracking center differential external Kalman filter (SCDEKF) to track the moving objects, and the method has higher accuracy and less computational load. In order to verify the advantages of proposed methods, a simulation environment was established in the Gazebo, and the common cameras were also added in simulation for comparison with the DVS sensor. The simulation results show that the 3-D improved $K$ -means method with DVS can cluster the moving objects accurately, and the SCDEKF can provide more accurate movement estimation than the traditional EKF method. Finally, two experiments were conducted to prove the methods' superiority. The main contribution is to solve the exploitation problems faced by the short-term borne sensor and promote its application in transportation.