Abstract: In an automated driving system, vision-based multi-target detection and trajectory tracking in front of the vehicle can provide effective information for pose estimation and behavior analysis of the front target. For the deficiency of multi-target trajectory tracking of integrated motion information and kernelized correlation filter tracking information, the convolutional neural network YOLOv2 is used to detect the target and a multi-target tracking method combining kernel correlation filtering and target motion information is proposed. The purpose is to integrate the motion information into the image feature tracking container so as to optimize the motion model and reduce the target tracking loss and deviation caused by the environment noise. An improved kernelized-correlation filter tracking scale invariance algorithm based on motion information is proposed. A multi-target detection tracking container is established, and a multi-target matching method combining target attribute, coincidence degree, motion state and tracking state is proposed. Experiments show that the proposed algorithm can achieve continuous real-time trajectory tracking of multiple targets in a certain scenario, and the average effective tracking rate is 92.5%

Key words: self-driving system; multi-target tracking; kernelized correlation filters(KCF); YOLOv2; Kalman filter