Ball-like observation model and multi-peak distribution estimation based particle filter for 3D Ping-pong ball tracking

3D ball tracking is of great significance to ping-pong game analysis, which can be utilized to applications such as TV content and tactic analysis. To achieve a high success rate in ping-pong ball tracking, the main problems are the lack of unique features and the complexity of background, which make it difficult to distinguish the ball from similar noises. This paper proposes a ball-like observation model and a multi-peak distribution estimation to improve accuracy. For the balllike observation model, we utilize gradient feature from the edge of upper semicircle to construct a histogram, besides, ball-size likelihood is proposed to deal with the situation when noises are different in size with the ball. The multi-peak distribution estimation aims at obtaining a precise ball position in case the partidles' weight distribution has multiple peaks. Experiments are based on ping-pong videos recorded in an official match from 4 perspectives, which in total have 122 hit cases with 2 pairs of players. The tracking success rate finally reaches 99.33%.