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HMR-vid: a comparative analytical survey on human motion recognition in video data

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Abstract

According to the rapid spread of multimedia data and online observations by users, the importance of researching on machine vision also, analyzing and automatic understanding of video data content is progressively increasing. Human motion recognition in video data is a crucial research subject in machine vision science that has plenty of applications, for instance, video surveillance, video indexing, robotics, human-computer interface and multimedia retrieval. Despite a high number of researches conducted on this topic, there is a necessity to achieve a more in-depth understanding, complete classification, and evaluation of existing human motion recognition stages. The novelty of this paper, our comparative analytical framework includes three major parts. Firstly, three different stages are introduced in recognizing human motion consisting of background subtraction, feature extraction, and machine learning classification. Secondly, five essential criteria are defined for evaluating the proposed human motion recognition methods. Finally, our comparative analysis of human motion recognition stages comprises two models. The analysis of background subtraction methods is based on applying the criteria for a qualitative comparison. Next, the feature extraction and machine learning classification methods are examined by specifying their main idea, benefits and challenges. Our comparative analytical framework can be beneficial for every researcher in this field by simplifying accurate selection and development of human motion recognition methods in future works.

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  1. Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

    Mohammad Reza Keyvanpour

  2. Computer Engineering and Data Mining Laboratory, Alzahra University, Tehran, Iran

    Shokofeh Vahidian & Mahin Ramezani

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Keyvanpour, M.R., Vahidian, S. & Ramezani, M. HMR-vid: a comparative analytical survey on human motion recognition in video data. Multimed Tools Appl 79, 31819–31863 (2020). https://doi.org/10.1007/s11042-020-09485-2

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