Indoor Activity Detection and Recognition for Sport Games Analysis
This work addresses automated analysis for sports games, specifically volleyball, by enhancing player activity recognition, but it is incremental as it builds on existing methods with contextual additions.
The paper tackled the problem of single player activity recognition in volleyball by incorporating geometry and contextual information via an activity context descriptor, which improved average classification performance from 77.56% by up to 18.35% on specific classes.
Activity recognition in sport is an attractive field for computer vision research. Game, player and team analysis are of great interest and research topics within this field emerge with the goal of automated analysis. The very specific underlying rules of sports can be used as prior knowledge for the recognition task and present a constrained environment for evaluation. This paper describes recognition of single player activities in sport with special emphasis on volleyball. Starting from a per-frame player-centered activity recognition, we incorporate geometry and contextual information via an activity context descriptor that collects information about all player's activities over a certain timespan relative to the investigated player. The benefit of this context information on single player activity recognition is evaluated on our new real-life dataset presenting a total amount of almost 36k annotated frames containing 7 activity classes within 6 videos of professional volleyball games. Our incorporation of the contextual information improves the average player-centered classification performance of 77.56% by up to 18.35% on specific classes, proving that spatio-temporal context is an important clue for activity recognition.