Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings

Research Article

Research on Trend Analysis Model of Movement Features Based on Big Data

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  • @INPROCEEDINGS{10.1007/978-3-030-19086-6_21,
        author={Hai Zou and Xiaofeng Xu},
        title={Research on Trend Analysis Model of Movement Features Based on Big Data},
        proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings},
        proceedings_a={ADHIP},
        year={2019},
        month={5},
        keywords={Big data Motion feature Human motion information},
        doi={10.1007/978-3-030-19086-6_21}
    }
    
  • Hai Zou
    Xiaofeng Xu
    Year: 2019
    Research on Trend Analysis Model of Movement Features Based on Big Data
    ADHIP
    Springer
    DOI: 10.1007/978-3-030-19086-6_21
Hai Zou1,*, Xiaofeng Xu2
  • 1: Zaozhuang Vocational College of Science and Technology
  • 2: Baoji Vocational and Technical College
*Contact email: hoiae251@sina.com

Abstract

The motion feature data capture can well preserve the details of the motion and truly record the trajectory of the motion. It has been widely used in many fields such as virtual reality, three-dimensional games, film and television effects, and so on. With the widespread application of motion feature capture, how to analyze the trend data of sports features has become a hot topic. The main purpose of the trend analysis of the research motion characteristics is to better understand and describe the motion process of the objects so as to manage and reuse the motion capture data in the motion capture database. For the existing motion feature capture data in the motion capture database, the motion feature data behavior is precisely segmented, the motion template is extracted and calculated more quickly and efficiently, the motion behavior is identified, and the motion behavior in the motion sequence segment is automatically identified.