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A Generalized Earley Parser for Human Activity Parsing and Prediction.

Siyuan Qi, Baoxiong Jia, Siyuan Huang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 7, 2020
    PubMed
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    This study introduces a generalized Earley parser for analyzing unlabeled video data, enabling accurate activity recognition and future prediction through context-free grammars.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Machine Learning

    Background:

    • Sequence data analysis, such as in videos, requires capturing complex, non-Markovian properties.
    • Traditional grammar parsers are limited to symbolic, segmented, and labeled inputs.
    • High-level semantic understanding is crucial for tasks like activity recognition and prediction.

    Purpose of the Study:

    • To generalize the Earley parser for parsing unlabeled and unsegmented sequence data.
    • To enable optimal segmentation and labeling of video data using context-free grammars.
    • To facilitate top-down future predictions based on parsing results.

    Main Methods:

    • Generalization of the Earley parser to handle probabilistic classifier outputs.
    • Application of context-free grammars for sequence data parsing.
    • Development of a method for top-down future predictions from parsed data.

    Main Results:

    • The generalized Earley parser successfully parses unlabeled and unsegmented sequence data.
    • The method achieves optimal segmentation and labeling based on input grammars.
    • Demonstrated effectiveness in human activity parsing and prediction across three video datasets.

    Conclusions:

    • The proposed generalized Earley parser is a generic, principled, and widely applicable method.
    • It significantly enhances the ability to parse and predict from complex sequence data.
    • The approach offers a robust framework for understanding and forecasting actions in videos.