Watch and Act: Dual Interacting Agents for Automatic Generation of Possession Statistics in Soccer

Document Type

Conference Article

Publication Title

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Abstract

Pass localization and team identification are two primary tasks for pass-count based possession statistics generation of a soccer match. While the existing works perform these two tasks separately, we propose dual interacting reinforcement learning agents to jointly perform these tasks. The proposed model has a localization agent, that decides which direction to move a temporal window to localize a pass. On the other hand, there is an identification agent that decides if the temporal window contains a pass for team-A (or team-B), or the localization agent needs to readjust the temporal window further. In this multi-agent setup, an agent may communicate by sharing some message to guide the other agent to achieve its task. To achieve this inter-agent communication, we extend the Dueling DQN architecture and share the value of a state as a message to the other agent. Two agents watch, act independently and cooperate with each other in order to detect a valid pass in a soccer video. A novel reward function is proposed that helps the agents to learn the optimal policy. Experiments performed on online videos show that our method is 3% better at localization of pass than the competitive methods.

First Page

3559

Last Page

3567

DOI

10.1109/CVPRW56347.2022.00400

Publication Date

1-1-2022

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