Team Tactics Estimation in Soccer Videos Based on a Deep Extreme Learning Machine and Characteristics of the Tactics

• Main Authors: Genki Suzuki, Sho Takahashi, Takahiro Ogawa, Miki Haseyama
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Sports video analysis; tactics estimation; deep learning; semantic analysis
• Online Access: https://ieeexplore.ieee.org/document/8863481/

A novel method for estimating team tactics in soccer videos based on a Deep Extreme Learning Machine (DELM) and unique characteristics of tactics is presented in this paper. The proposed method estimates the tactics of each team from players' formations and enables successful training from a limited amount of training data. Specifically, the estimation of tactics consists of two stages. First, by utilizing two DELMs corresponding to the two teams, the proposed method estimates the provisional tactics of each team. Second, the proposed method updates the team tactics based on unique characteristics of soccer tactics, the relationship between tactics of the two teams and information on ball possession. Consequently, since the proposed method estimates the team tactics that satisfy these characteristics, accurate estimation results can be obtained. In an experiment, the proposed method is applied to actual soccer videos to verify its effectiveness.