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In this paper we address the problem of motion event detection in athleterecordings from individual sports. In contrast to recent end-to-end approaches,we propose to use 2D human pose sequences as an intermediate representationthat decouples human motion from the raw video information. Combined withdomain-adapted athlete tracking, we describe two approaches to event detectionon pose sequences and evaluate them in complementary domains: swimming andathletics. For swimming, we show how robust decision rules on pose statisticscan detect different motion events during swim starts, with a F1 score of over91% despite limited data. For athletics, we use a convolutional sequence modelto infer stride-related events in long and triple jump recordings, leading tohighly accurate detections with 96% in F1 score at only +/- 5ms temporaldeviation. Our approach is not limited to these domains and shows theflexibility of pose-based motion event detection.

Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings