Event state based particle filter for ball event detection in volleyball game analysis

Abstract

The ball state tracking and detection technology plays a significant role in volleyball game analysis for volleyball team supporting and tactics development. This paper proposes a ball event detection method to achieve high detection rate by solving challenges including: the great variety of event length, the large intra-class difference of one event and the influence caused by ball trajectories. Proposed state vector covers both the event type and the event period length so that the system model can transits various lengths of event period and predicts event types by volleyball game rules. The curve segmental observation model avoids the tracking error influence to evaluate the event period likelihood by referring neighbouring trajectories of the ball. And according to the standard of the ball event, the feature of the distance between the ball and specific court line are extracted to evaluate the ball event type in observation. At last a two-layer estimation method estimates the posterior state which is a joint probability distribution. Experiments of the proposed method implemented on 3D trajectories tracked from multi-view volleyball game videos shows the detection rate reaches 90.43%.