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HiGCIN: Hierarchical Graph-Based Cross Inference Network for Group Activity Recognition.

Rui Yan, Lingxi Xie, Jinhui Tang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 27, 2020

Summary

This summary is machine-generated.

This study introduces a novel Hierarchical Graph-based Cross Inference Network (HiGCIN) for group activity recognition (GAR). The HiGCIN effectively infers group behaviors by integrating multi-level visual cues without needing individual action labels.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Group activity recognition (GAR) is complex, requiring integration of individual cues and interactions.
Existing methods often struggle with capturing intricate relationships within group dynamics.

Purpose of the Study:

To develop an advanced model for group activity recognition.
To improve the inference of group behaviors by considering multi-level information.
To reduce the annotation burden in data preparation for GAR.

Main Methods:

Proposed a Hierarchical Graph-based Cross Inference Network (HiGCIN) for end-to-end group activity recognition.
Introduced a Cross Inference Block (CIB) to capture spatiotemporal dependencies at body-region and person levels.
Designed modules for feature extraction and refinement across hierarchical levels (body-region, person, group-activity).

Main Results:

Demonstrated the effectiveness of HiGCIN on popular benchmarks.
Showcased the model's capability in inferring group activities using multi-level visual cues.
Validated the approach's efficiency by eliminating the need for individual action labels during training.

Conclusions:

HiGCIN offers a robust and efficient solution for group activity recognition.
The hierarchical, cross-inference approach effectively models complex group interactions.
Reduced data annotation requirements make the method more practical for real-world applications.