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Heatmap Pooling Network for Action Recognition From RGB Videos.

Mengyuan Liu, Jinfu Liu, Yongkang Jiang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 5, 2025
    PubMed
    Summary

    This study introduces HP-Net, a novel heatmap pooling network for human action recognition (HAR) in videos. HP-Net extracts robust features, outperforming existing methods and enabling more accurate action identification.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Human action recognition (HAR) in videos is crucial but faces challenges with RGB data, including redundancy, noise, and high storage costs.
    • Existing deep feature extraction methods struggle to efficiently utilize rich information present in videos.

    Purpose of the Study:

    • To propose a novel heatmap pooling network (HP-Net) for robust and concise feature extraction in human action recognition.
    • To enhance action recognition accuracy by integrating extracted features with multimodal data.

    Main Methods:

    • Developed a heatmap pooling network (HP-Net) with a feedback pooling module for information-rich, robust, and concise human body feature extraction.
    • Designed spatial-motion co-learning and text refinement modulation modules for multimodal data integration.

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    Main Results:

    • The extracted pooled features from HP-Net showed significant performance advantages over traditional pose and heatmap features.
    • HP-Net consistently outperformed existing human action recognition methods across multiple benchmark datasets (NTU RGB+D 60/120, Toyota-Smarthome, UAV-Human).

    Conclusions:

    • HP-Net effectively addresses the limitations of existing HAR methods by providing superior feature extraction and integration capabilities.
    • The proposed approach offers a more robust and efficient solution for human action recognition from videos.