Extract Soccer Video Highlights with Modified CRF

Soccer video analysis is attracting much attention. In this paper, we propose a modified conditional random field (CRF) model to extract the highlights of an entire soccer video. Highlight extraction in soccer video is essentially a kind of timing annotation problems, so the commonly used CRF model is adopted to solve this problem in this paper. Meanwhile, Boolean function is normally used as feature function in the CRF model, which will result in a hard association between observed variables and highlight variables. By introducing Bayesian network to model the observed variables and replacing the original feature function with posterior probability calculated with Bayesian network, hard association is transformed into soft association, which makes the model more close to the actual situation. Experimental results show that the proposed algorithm has achieved good results.

A VISUAL FRAMEWORK FOR INTERACTION DETECTION IN SOCCER MATCHES

In the last decade, soccer video analysis has received a lot of attention from the scientific community. This increasing interest is motivated by the possible applications over a wide spectrum of topics: indexing, summarization, video enhancement, team and players statistics, tactics analysis, referee support, etc. The application of computer vision methodologies in the soccer context requires many problems to be faced: ball and players have to be detected in the images in any light and weather condition, they have to be localized in the field, tracked over time and finally their interactions have to be detected and analyzed. The latter task is fundamental, especially for statistic and referee decision support purposes, but, unfortunately, it has not received adequate attention from the scientific community and a lot of research remains to be done. In this paper a multicamera system is presented to detect the ball player interactions during soccer matches. The proposed method extracts, by triangulation from multiple cameras, the 3D ball and player trajectories and, by estimating the trajectory intersections, detects the ball-player interactions. An inference process is then introduced to determine the player kicking the ball and to estimate the interaction frame. The system was tested during several matches of the Italian first division football championship and experimental results demonstrated that the proposed method is robust and accurate.