LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition (2410.21108v2)

Abstract: Group Activity Recognition (GAR) remains challenging in computer vision due to the complex nature of multi-agent interactions. This paper introduces LiGAR, a LIDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition. LiGAR leverages LiDAR data as a structural backbone to guide the processing of visual and textual information, enabling robust handling of occlusions and complex spatial arrangements. Our framework incorporates a Multi-Scale LIDAR Transformer, Cross-Modal Guided Attention, and an Adaptive Fusion Module to integrate multi-modal data at different semantic levels effectively. LiGAR's hierarchical architecture captures group activities at various granularities, from individual actions to scene-level dynamics. Extensive experiments on the JRDB-PAR, Volleyball, and NBA datasets demonstrate LiGAR's superior performance, achieving state-of-the-art results with improvements of up to 10.6% in F1-score on JRDB-PAR and 5.9% in Mean Per Class Accuracy on the NBA dataset. Notably, LiGAR maintains high performance even when LiDAR data is unavailable during inference, showcasing its adaptability. Our ablation studies highlight the significant contributions of each component and the effectiveness of our multi-modal, multi-scale approach in advancing the field of group activity recognition.

• The paper introduces LiGAR, a novel framework that integrates LiDAR with visual and textual modalities to enhance group activity recognition, achieving state-of-the-art results.
• It employs a Multi-Scale LiDAR Transformer, Cross-Modal Guided Attention, and an Adaptive Fusion Module to synergistically process multi-modal inputs at various granularities.
• The framework demonstrates robust performance in challenging conditions and paves the way for advancements in surveillance, sports analytics, and smart city applications.

LiGAR: Advancing Group Activity Recognition through Multi-Modal Integration

The paper introduces the LiDAR-Guided Hierarchical Transformer (LiGAR) framework, a significant contribution to the domain of Group Activity Recognition (GAR). LiGAR distinguishes itself by integrating LiDAR data with visual and textual information, providing a comprehensive multi-modal approach to address the challenges inherent in recognizing complex group activities.

Framework Overview

LiGAR employs a hierarchical transformer model, using LiDAR data as the structural backbone for processing visual and textual modalities. Key components of the framework include the Multi-Scale LiDAR Transformer (MLT), Cross-Modal Guided Attention (CMGA), and an Adaptive Fusion Module (AFM). These components are designed to function synergistically to enhance the understanding and interpretation of group activities at various levels of granularity—from individual actions to whole-scene dynamics. The framework's adaptability ensures robust performance even in scenarios with absent LiDAR data during inference, demonstrating its flexibility and robustness across different environments.

Methodological Insights

1. Multi-Scale LiDAR Transformer: LiGAR processes LiDAR data at multiple scales, capturing detailed spatial information which enhances the detection of occlusions and complex spatial arrangements. This transforms the spatial data into a form more amenable to integrating with other modalities.
2. Cross-Modal Guided Attention: The CMGA mechanism utilizes the encoded LiDAR features to guide the attention mechanisms in both the visual and textual streams. This cross-modal interaction not only improves alignment across different data sources but also enhances the model's interpretative power—enabling it to focus on scene elements most relevant to the activities being recognized.
3. Adaptive Fusion Module: Utilizing TimeSformer architecture, the AFM dynamically adjusts the weighting of different modalities, ensuring that the most informative data is prioritized at any given time. This capability is crucial for maintaining high recognition accuracy amid the varying conditions present in real-world environments.
4. Hierarchical Activity Decoder: The framework employs a hierarchical decoding approach for predicting activities at multiple semantic levels. This model aligns with the multi-granular nature of group activities and ensures the predictions are consistent and context-aware.

Empirical Evaluation

The LiGAR framework demonstrates superior performance across multiple GAR datasets. It achieves state-of-the-art results on JRDB-PAR, Volleyball, and NBA datasets, with notable improvements including a 10.6% increase in F1-score on JRDB-PAR and a 5.9% improvement in Mean Per Class Accuracy on the NBA dataset. These results underscore the efficacy of LiGAR's multi-modal, hierarchical architecture in capturing the dynamics of complex group interactions.

Contributions and Implications

LiGAR sets a new benchmark in GAR by effectively demonstrating the advantages of integrating LiDAR with visual and textual data. This integration allows for robust detection of group activities even under challenging conditions, such as occlusions common in dynamic environments. The framework's adaptability across various conditions potentially paves the way for future applications in surveillance, sports analytics, and smart city implementations.

Future Directions

The research opens several avenues for further exploration. These include extending LiGAR's adaptability to scenarios where LiDAR may not be available or practical, enhancing its feature extraction capabilities, and diversifying its context comprehension by integrating additional data sources or leveraging innovations in transformer architectures. Future work could focus on improving unsupervised learning techniques within the framework to reduce dependency on annotated datasets.

In conclusion, LiGAR not only advances the state-of-the-art in multi-modal GAR but also sets a foundation for exploring more adaptable and comprehensive AI systems. Its holistic approach in processing multi-scale and multi-modal data signals a paradigm shift in how complex group activities are interpreted and recognized in diverse applications.