Extracting Key Sub-trajectory Features for Supervised Tactic Detection in Sports Video
Tactic analysis is receiving more attention in sports video analysis for its assistance to coaches and players. This paper proposes an efficient key sub-trajectory feature representation of ball trajectory for tactic analysis. Ball trajectories are modeled with the generalized suffix tree where frequent sub-trajectory patterns are searched for. Key sub-trajectory patterns are extracted by further filtering these frequent sub-trajectory patterns. Instead of directly using individual sub-trajectories as features to train tactic detectors, we take key sub-trajectory patterns as a whole. Key sub-trajectory feature representation effectively removes noise, reduces the dimension of features, and improves the performance of supervised learning to detect tactics.