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      Video behavior recognition based on locally compressive sensing

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          Abstract

          Compressive sensing has been successfully applied in the field of target tracking but not for behavior recognition. This paper presents a locally compressive sensing algorithm for behavior analysis which combines compressive tracking and centroid localization for recognition of video object behavior. Local compressive sensing selects a behavior-sensitive area for compressive tracking which characterizes target behavior based on classification of the object trajectory and local centroid velocity. Tests show that locally compressive sensing can accurately recognize global behavior such as running and falling and local behavior such as smiles and blinking. Therefore, the locally compressive sensing method is of great value that can be used for video surveillance and behavior recognition.

          Abstract

          摘要 压缩感知在目标跟踪领域已取得成功应用, 但其在行为识别领域的研究尚不成熟.该文提出了局部压缩感知的思想, 结合压缩跟踪与质心定位, 实现了视频目标行为的有效识别。局部压缩感知是选定行为敏感区域进行压缩跟踪, 基于区域质心轨迹和速度的计算与分类, 对目标行为进行认知计算。实验结果表明:借助局部压缩感知, 能实现一些特殊的全局目标行为 (如奔跑、跌倒等) 和局部目标行为 (如微笑、眨眼等) 的识别, 并保证了识别率及识别精度。因此, 该文提出的局部压缩感知方法在视频监控目标行为识别领域的应用具有一定的探索意义与研究价值。

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          Author and article information

          Journal
          J Tsinghua Univ (Sci & Technol)
          Journal of Tsinghua University (Science and Technology)
          Tsinghua University Press
          1000-0054
          15 June 2018
          21 June 2018
          : 58
          : 6
          : 581-586
          Affiliations
          1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
          2Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
          3School of Information Science and Engineering, Xiamen University, Xiamen 361005, China
          4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
          Author notes
          *Corresponding author: CHEN Xi, E-mail: chenxi@ 123456ms.xjb.ac.cn
          Article
          j.cnki.qhdxxb.2018.25.024
          10.16511/j.cnki.qhdxxb.2018.25.024
          Copyright © Journal of Tsinghua University

          This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

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