  In this paper we address the problem of motion event detection in athlete
recordings from individual sports. In contrast to recent end-to-end approaches,
we propose to use 2D human pose sequences as an intermediate representation
that decouples human motion from the raw video information. Combined with
domain-adapted athlete tracking, we describe two approaches to event detection
on pose sequences and evaluate them in complementary domains: swimming and
athletics. For swimming, we show how robust decision rules on pose statistics
can detect different motion events during swim starts, with a F1 score of over
91% despite limited data. For athletics, we use a convolutional sequence model
to infer stride-related events in long and triple jump recordings, leading to
highly accurate detections with 96% in F1 score at only +/- 5ms temporal
deviation. Our approach is not limited to these domains and shows the
flexibility of pose-based motion event detection.
