Pixels or Positions? Benchmarking Modalities in Group Activity Recognition

TL;DR

This paper introduces SoccerNet-GAR, a benchmark dataset for comparing video and tracking modalities in group activity recognition, revealing tracking's superior efficiency and accuracy in sports contexts.

Contribution

The work provides a new multimodal dataset, a unified evaluation protocol, and a novel role-aware graph neural network architecture for tracking-based group activity recognition.

Findings

Tracking-based classifiers outperform video-based ones in accuracy.

Tracking models require significantly less training time and fewer parameters.

The dataset enables standardized comparison of modalities in sports group activity recognition.

Abstract

Group Activity Recognition (GAR) is well studied on the video modality for surveillance and indoor team sports (e.g., volleyball, basketball). Yet, other modalities such as agent positions and trajectories over time, i.e. tracking, remain comparatively under-explored despite being compact, agent-centric signals that explicitly encode spatial interactions. Understanding whether pixel (video) or position (tracking) modalities leads to better group activity recognition is therefore important to drive further research on the topic. However, no standardized benchmark currently exists that aligns broadcast video and tracking data for the same group activities, leading to a lack of apples-to-apples comparison between these modalities for GAR. In this work, we introduce SoccerNet-GAR, a multimodal dataset built from the $64$ matches of the football World Cup 2022. Specifically, the broadcast videos and player tracking modalities for $87{,}939$ group activities are synchronized and annotated with $10$ categories. Furthermore, we define a unified evaluation protocol to benchmark two strong unimodal approaches: (i) competitive video-based classifiers and (ii) tracking-based classifiers leveraging graph neural networks. In particular, our novel role-aware graph architecture for tracking-based GAR directly encodes tactical structure through positional edges connecting players by their on-pitch roles. Our tracking model achieves $77.8\%$ balanced accuracy compared to $60.9\%$ for the best video baseline, while training with $7 \times$ less GPU hours and $479 \times$ fewer parameters ($180K$ vs. $86.3M$). This study provides new insights into the relative strengths of pixels and positions for group activity recognition in sports.