Learning Actor Relation Graphs for Group Activity Recognition (1904.10117v1)

Abstract: Modeling relation between actors is important for recognizing group activity in a multi-person scene. This paper aims at learning discriminative relation between actors efficiently using deep models. To this end, we propose to build a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture the appearance and position relation between actors. Thanks to the Graph Convolutional Network, the connections in ARG could be automatically learned from group activity videos in an end-to-end manner, and the inference on ARG could be efficiently performed with standard matrix operations. Furthermore, in practice, we come up with two variants to sparsify ARG for more effective modeling in videos: spatially localized ARG and temporal randomized ARG. We perform extensive experiments on two standard group activity recognition datasets: the Volleyball dataset and the Collective Activity dataset, where state-of-the-art performance is achieved on both datasets. We also visualize the learned actor graphs and relation features, which demonstrate that the proposed ARG is able to capture the discriminative relation information for group activity recognition.

• The paper introduces a novel Actor Relation Graph (ARG) that automatically learns multi-actor relationships from video data to enhance group activity recognition.
• It integrates spatially localized and temporal randomized graphs with Graph Convolutional Networks, improving inference efficiency and reducing overfitting.
• Empirical evaluations on the Volleyball and Collective Activity datasets demonstrate state-of-the-art accuracy, validating its practical applicability.

Overview of "Learning Actor Relation Graphs for Group Activity Recognition"

The paper "Learning Actor Relation Graphs for Group Activity Recognition" addresses the intricate challenge of recognizing group activities in multi-person scenes by strategically modeling the relationships between actors using deep learning techniques. The foundation of this work is the introduction of the Actor Relation Graph (ARG), an innovative approach that facilitates end-to-end learning of actor relationships from video data. This framework captures both appearance and positional relations between actors, leveraging Graph Convolutional Networks (GCN) for efficient inference, signaling a significant advancement over prior methods that utilized rigid manual specifications for graphical models or costly message-passing schemes.

Key Contributions and Methodological Approach

The research introduces several methodological innovations:

1. Actor Relation Graph (ARG): The ARG is a flexible, automatic graph-based model designed to encapsulate interactions within videos. Nodes in the graph denote actor features, while edges represent their mutual relations. This model’s flexibility enables it to integrate seamlessly atop existing 2D CNNs, allowing the combined framework to exploit graphical data structures for robust group activity recognition.
2. Graph Variants for Sparsity: To enhance the effectiveness of ARGs across temporal sequences, spatially localized ARGs constrain actor connections to local neighborhoods, while temporal randomized ARGs introduce diversity through strategically sampling frames, markedly curbing computational demands and overfitting.
3. Multigraph System: Building on the unique signaling strengths of ARGs, the authors propose a multigraph system to account for varied relational cues across actors, advancing relational reasoning capabilities.
4. Efficient GCN Inference: Through refined GCN operations, the approach not only bolsters actor interaction reasoning but also maintains computational efficiency—critical for practical deployment in video analysis applications.

Empirical Evaluation and Results

The extensive empirical evaluation of the model was conducted on two benchmark datasets: the Volleyball dataset and the Collective Activity dataset. The empirical results demonstrate state-of-the-art performance, underscoring the model’s efficacy in leveraging relational data for group activity discernment. For instance, when applied to the Volleyball dataset, the model achieved superior group activity recognition accuracy compared to existing methodologies. Such performance is attributable to the ARG’s adeptness in integrating multi-faceted relational data into cohesive scene understanding.

Implications and Future Directions

Practically, this research holds significant promise for applications in video surveillance, sports analysis, and social behavior understanding, where accurate recognition of group activities is paramount. Theoretically, the introduction of ARGs and their integration with GCNs offers a fertile ground for future exploration into more granular inter-actor dynamics and complex relational structures.

Future research could endeavor to explore the scalability of ARGs in larger datasets or leverage this framework in real-time scenarios, potentially integrating it with other modalities such as audio or textual data for enhanced context-awareness. Additionally, expanding the framework to accommodate dynamically changing environments or actor identities could further enhance its applicability across diverse video analytics contexts.

The paper's contributions underscore the importance of relational modeling in advancing the state of the art in group activity recognition, setting a benchmark for future innovation in the field.