Multi-object tracking for horse racing

Abstract

Multi-object tracking has been extensively studied in pedestrian behavior analysis and vehicle analysis, but has yet to be well extended to the more challenging horse racing scenarios. This work makes the first attempt to systematically analyze challenges for horse racing tracking problems, including occlusion, trajectory staggered, often switching of cameras (i.e., angles of views), and blurred sprint caused by horse competition. An augmentation-based multi-object tracking method (GMOT) is proposed to solve the above-mentioned challenges. The auxiliary classifier generative adversarial network is adopted in GMOT to augment the horse racing data to enhance target detection and re-identification results. Horse racing videos recorded in day and night scenes are both tested in our experiments. Experimental results show that the proposed method yields good results.