Video-Based Soccer Ball Detection in Difficult Situations
The interest in video-based systems for acquiring and analyzing player and ball data of soccer games is increasing in several domains such as media and professional training. Consequently, tracking systems for live acquisition of quantitative motion data are becoming widely used. The interest for such systems is especially high for training purposes but the demands concerning the precision of the data are very high. Current systems reach a satisfying precision due to heavy interaction of operators. In order to increase the level of automation while retaining a constantly high precision, more robust tracking systems are required. However, this demand is accompanied by an increasing trend to stand-alone, mobile, low-cost soccer tracking systems due to cost concerns, stadium infrastructure, media rights etc. As a consequence, the live data acquisition has to be accomplished by using only a few cameras so that there are generally only few perspectives of the players and the ball. In addition, only low-resolution images are available in many cases. The low-resolution images strongly exacerbate the problem of detection and tracking the soccer ball. Apart from the challenge that arises from the appearance of the ball, situations where the ball is occluded by the players make the detection of the ball difficult. The lower the number of the cameras is, the lower generally is the number of available perspectives - and thus the more difficult it is to gather precise motion data. This paper presents a soccer ball detection approach that is applicable to difficult situations such as occluded cases. It handles low-resolution images from single static camera systems and can be used e.g. for ball trajectory reconstruction. The performance of the approach is analyzed on a data set of a Bundesliga match.
KeywordsSoccer Sport analysis Ball detection Video-tracking system
- 1.Herrmann, C., Manger, D., Metzler J.: Feature-based localization refinement of players in soccer using plausibility maps. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition IPCV (WORLDCOMP), vol. 2, Las Vegas (2011)Google Scholar
- 4.Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imag. Spec. Iss. Video Object Process. 11(3), 172–185 (2004)Google Scholar
- 7.Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Sebastopol (2008)Google Scholar