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Activity recognition in sport is an attractive field for computer visionresearch. Game, player and team analysis are of great interest and researchtopics within this field emerge with the goal of automated analysis. The veryspecific underlying rules of sports can be used as prior knowledge for therecognition task and present a constrained environment for evaluation. Thispaper describes recognition of single player activities in sport with specialemphasis on volleyball. Starting from a per-frame player-centered activityrecognition, we incorporate geometry and contextual information via an activitycontext descriptor that collects information about all player's activities overa certain timespan relative to the investigated player. The benefit of thiscontext information on single player activity recognition is evaluated on ournew real-life dataset presenting a total amount of almost 36k annotated framescontaining 7 activity classes within 6 videos of professional volleyball games.Our incorporation of the contextual information improves the averageplayer-centered classification performance of 77.56% by up to 18.35% onspecific classes, proving that spatio-temporal context is an important clue foractivity recognition.

Indoor Activity Detection and Recognition for Sport Games Analysis