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Object Activity Scene Description, Construction, and Recognition.

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    This study introduces a new method for recognizing complex human activities using primitive actions and joint trajectories. The approach utilizes a convolutional neural network (CNN) for efficient and accurate activity scene recognition.

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    Area of Science:

    • Computer Science
    • Robotics
    • Artificial Intelligence

    Background:

    • Action recognition is crucial for social robots.
    • 3-D skeleton-based methods are popular but struggle with complex activities.
    • Recognizing group actions within activity scenes remains challenging.

    Purpose of the Study:

    • To develop an effective method for recognizing complex human activities in scenes.
    • To address the limitations of existing approaches in group action recognition.

    Main Methods:

    • Scene partitioning into primitive actions (PAs) using motion attention.
    • Describing PAs via trajectory vectors of joints.
    • Employing a convolutional neural network (CNN) for activity scene recognition, treating joint motions as 'words'.

    Main Results:

    • The proposed approach demonstrates efficiency in recognizing human activity scenes.
    • Experimental results validate the effectiveness of the CNN-based method.

    Conclusions:

    • The novel method successfully recognizes complex human activities by decomposing them into primitive actions.
    • The approach offers a promising direction for enhancing robot interaction with human environments.