From soccer video to ball possession statistics

Article Type

Research Article

Publication Title

Pattern Recognition


Ball possession statistics in a soccer match is evaluated by counting the number of valid passes by both teams. The valid passes are determined by monitoring the start and end of a ball passing event initiated by a player. In this work, we map pass detection as detection of split and merge of nodes of a flow network. The players and ball represent nodes in the network. A group is formed by the objects (ball and players) which are spatially close to each other. Objects belonging to the same group are allowed to split or merge. We use this group relation to check if the objects split or merge in the sequence of frames. A constraint is added to the network to make sure that two objects can split only if the objects were previously merged. Flow through the split or merge node of the network denotes a ball pass event. Additional nodes like appear and disappear are added to the network to map the possibility that new objects could appear or old objects may disappear to and from the frame. The minimum cost path in the flow network provides the solution for valid pass events. Experimental evaluation shows that our proposal is at least 4% better in estimating ball possession statistics and 8% better in pass detection of a soccer match seen in a broadcast video than that of competitive methods.



Publication Date


This document is currently not available here.