Actor-Transformers for Group Activity Recognition (2003.12737v1)

Abstract: This paper strives to recognize individual actions and group activities from videos. While existing solutions for this challenging problem explicitly model spatial and temporal relationships based on location of individual actors, we propose an actor-transformer model able to learn and selectively extract information relevant for group activity recognition. We feed the transformer with rich actor-specific static and dynamic representations expressed by features from a 2D pose network and 3D CNN, respectively. We empirically study different ways to combine these representations and show their complementary benefits. Experiments show what is important to transform and how it should be transformed. What is more, actor-transformers achieve state-of-the-art results on two publicly available benchmarks for group activity recognition, outperforming the previous best published results by a considerable margin.

• The paper proposes actor-transformers for group activity recognition, using implicit spatio-temporal modeling via self-attention to refine actor information without predefined spatial structures.
• The methodology fuses static 2D pose features with dynamic 3D motion features, using the actor-transformer and attention to refine actor relationships for group activity prediction.
• Empirical evaluation shows actor-transformers achieve state-of-the-art performance on the Volleyball and Collective datasets, significantly improving accuracy over prior methods.

Actor-Transformers for Group Activity Recognition: An Expert Analysis

This paper presents a novel approach for group activity recognition by employing actor-transformers, a technique inspired by advancements in natural language processing, specifically transformers and self-attention mechanisms. The research addresses the task of understanding individual and collective activities from video streams, a key challenge in domains such as surveillance, sports analytics, and crowd monitoring. Existing methodologies for activity recognition have traditionally relied on modeling spatial and temporal dynamics through explicit relations of actor positions. However, this paper introduces an implicit spatio-temporal approach using transformers, which refines actor-specific information without predefined spatial structures.

The authors of this paper leverage a fusion of static and dynamic actor representations. The static representation is derived from a 2D pose network that extracts pose features from single frames, while the dynamic representation incorporates motion information through a 3D CNN processing sequences of RGB or optical flow frames. This dual representation encoding captures both the actor's pose and movement, facilitating a comprehensive understanding of activity dynamics. The actor-transformer model uses these encoded features to perform attention-based refinement of actor relationships, ultimately allowing for effective group activity prediction.

The empirical evaluation conducted in the paper demonstrates the effectiveness of the actor-transformers, with the model achieving state-of-the-art performance on two benchmarks: the Volleyball and Collective datasets. These datasets are well-established within the community for evaluating models on group activity tasks. The reported numerical results show a significant improvement over previous methods, highlighting the actor-transformers' capacity to process and reason about actor interactions through self-attention without explicitly modeling spatial relations.

The theoretical implications of using actor-transformers for group activity recognition are noteworthy. The self-attention mechanism enables the model to focus on relevant actor interactions dynamically, which may lead to more generalized applications beyond the tasks explored in this paper. Practically, this approach could enhance automated systems requiring real-time analysis of group behaviors, such as advanced security systems or sports performance analytics. Future developments may involve extending the transformer architecture further to integrate decoder components or explore additional fusion strategies among actor representations.

In summary, this paper contributes a robust approach for recognizing collective activities using actor-transformers, achieving notable advances in prediction accuracy while simplifying model development by removing explicit spatial dependency constructions. The results suggest the potential of transformers to extend beyond their initial application in NLP to complex video analysis tasks, with significant implications for advancing artificial intelligence in understanding human actions within group contexts.