Team Tactics Estimation in Soccer Videos via Deep Extreme Learning Machine Based on Players Formation

A method of team tactics estimation in soccer videos is presented in this paper. Our method enables estimation of basic tactics in each team on the basis of the Deep-Extreme Learning Machine (DELM) by using features of players formation. In the soccer games, team tactics relate to each other team. Therefore, the proposed method obtains final estimation results by utilizing two DELMs of each team and their relationship. Since the proposed method takes into consideration the relevance of the estimated tactics in each team, we realize accurate tactics estibimation. Experimental results using actual soccer videos showed the effectiveness of our method.

[1]  Miki Haseyama,et al.  Decision level fusion-based team tactics estimation in soccer videos , 2016, 2016 IEEE 5th Global Conference on Consumer Electronics.

[2]  Victor C. M. Leung,et al.  Extreme Learning Machines [Trends & Controversies] , 2013, IEEE Intelligent Systems.

[3]  Qi Tian,et al.  Trajectory-based ball detection and tracking with applications to semantic analysis of broadcast soccer video , 2003, MULTIMEDIA '03.

[4]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[5]  Chng Eng Siong,et al.  Automatic replay generation for soccer video broadcasting , 2004, MULTIMEDIA '04.