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Image Multi-human Behavior Analysis Based on Low Rank Texture Direction

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Abstract

The main task of computer vision is to understand the behavioral meaning of the target in the image or video. In this paper, we present a method of human behavior analysis based on low rank texture direction. Faster-RCNN network model is chose to locate multiple human targets in complex scene images, also the global contrast saliency method was used to highlight the human body’s target and weaken the background information in an image. Transform Invariant Low Rank Textures(TITL) method was used to extract the target of Low rank texture and get the goal of overall Low rank texture direction as human behavior descriptor. In this paper, the target behavior analysis experiment was carried out under a public dataset. The experiment including the human body segmentation and storage area, calibration of the direction of the behavior of how the human body target in the video. The calculation of human behavior trend is carried out as well.

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Authors and Affiliations

  1. China University of Mining and Technology, Ding 11, Xueyuan Road, Haidian District, Beijing, China

    Mengjun Li & Guoying Zhang

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  1. Mengjun Li
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  2. Guoying Zhang
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Correspondence to Mengjun Li.

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Li, M., Zhang, G. Image Multi-human Behavior Analysis Based on Low Rank Texture Direction. J Sign Process Syst 90, 1245–1255 (2018). https://doi.org/10.1007/s11265-018-1344-0

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  • DOI: https://doi.org/10.1007/s11265-018-1344-0

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