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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.
Bibliographical noteFunding Information:
This work is supported in part by the National Natural Science Foundation of China under Grant 62202175 and 61876066 , in part by the 67th Chinese Postdoctoral Science Foundation ( 2020M672631 ), and in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).
- Adversarial generation network
- Horse racing
- Multi-object tracking
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- 1 Active
KWONG, S. T. W., KUO, C. J., WANG, S. & ZHANG, X.
1/01/21 → 30/06/24
Project: Grant Research