Enhancement of Speed and Accuracy Trade-Off for Sports Ball Detection in Videos—Finding Fast Moving, Small Objects in Real Time

Sensors

Mario Hlawitschka¹  Alexander Hiemann²  Thomas Kautz³  Tino Zottmann⁴ 

[1] Faculty of Computer Science and Media, Leipzig University of Applied Sciences, 04277 Leipzig, Germany;Institute of Computer Science, Leipzig University, 04109 Leipzig, Germany;Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany;Media Seasons, 04105 Leipzig, Germany;

关键词: sports ball detection;    small object detection;    YOLO;    data augmentation;    speed-accuracy trade-off;   

DOI  :  10.3390/s21093214

来源: DOAJ

【 摘 要 】

The detection and localization of the ball in sport videos is crucial to better understand events and actions occurring in those sports. Despite recent advances in the field of object detection, the automatic detection of balls remains a challenging task due to the unsteady nature of balls in images. In this paper, we address the detection of small, fast-moving balls in sport video data and introduce a real-time ball detection approach based on the YOLOv3 object detection model. We apply specific adjustments to the network architecture and training process in order to enhance the detection accuracy and speed: We facilitate an efficient integration of motion information, avoiding a complex modification of the network architecture. Furthermore, we present a customized detection approach that is designed to primarily focus on the detection of small objects. We integrate domain-specific knowledge to adapt image pre-processing and a data augmentation strategy that takes advantage of the special features of balls in images in order to improve the generalization ability of the detection network. We demonstrate that the general trade-off between detection speed and accuracy of the YOLOv3 model can be enhanced in consideration of domain-specific prior knowledge.

【 授权许可】

Unknown