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A video-based tracking system for football player analysis using Efficient Convolution Operators

Computer vision has been applied in sports analysis under the demand of the media as well as a training activity. This paper presents work on a system for tracking multiple football players in video streams. The challenges of the task are: the players are relatively small in the video with chaos movements; the processing time is efficient to ensure the analyzed data is reported during the match while the accuracy is required to be sufficient; the hardware of the system needs to be high mobility. To overwhelm those, we apply Efficient Convolution Operators (ECO) as a core tracking method to track the targets on two synchronized laptops, then the data is merged in a post-processing stage. Besides, user interactive functions are also provided to assist the operators to correct failed tracks. The tracking method is qualitatively evaluated on videos from professional football matches with two resolution settings. The number of user interactions to correct the failed tracks and the time processing are chosen as criteria for the evaluation. The results show that ECO tracking outperforms several well-known tracking methods with less than 1 tracking loss in 2 minutes on average with processing rate of 12–17 fps. In conclusion, the proposed system is a promising tool for football player tracking and statistical analysis in practice.