A Multi-target Tracking Algorithm for Fast-moving Workpieces Based on Event Camera

Multi-target tracking application for fast-moving workpieces has drawn increasing attention in the industrial field. For the dense, fast moving workpieces with few texture features, traditional cameras get poor quality images with dynamic blur and object adhesion, which makes the detection and tracking of workpieces unreliable. However, the event camera outputs events asynchronously at a microsecond speed when the pixel intensity changes, which can capture the contours of fast-moving workpieces well. In this paper, we propose a parallel two-pipe multi-target tracking algorithm based on the event camera for fast-moving workpieces. RGB-E image obtained by fusing the RGB image and the event solves the unreliable detection caused by dynamic blur and object adhesion. The parallel mechanism ensures that the low-speed detection pipeline does not have much impact on the speed of the high-speed tracking pipeline. Hungarian algorithm is used to associate the detection results obtained by the YOLOv4-tiny detector with the tracking results obtained by the KCF tracker. A correction algorithm based on pixel speed is proposed to synchronize detection results and tracking results. Experimental results prove the proposed algorithm can achieve reliable detection and tracking performance for fastmoving workpieces.