基于动态帧间间隔更新的人群异常行为检测
作者:
基金项目:

省科技厅区域科技重大项目(2015H4007)


Abnormal Crowd Behavior Detection Based on Dynamic Interframe Spacing Updating
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [12]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    针对视频监控中人群异常行为检测方面存在的实时性和准确性问题,本文基于金字塔LK光流法提出一种动态帧间间隔更新的人群异常行为检测的方法. 该算法通过提取的人群运动信息来动态更新帧间间隔,接着以该帧间间隔来检测人群运动信息. 这样,算法不仅保留了原算法在检测人群运动信息方面优点,且有效提高了算法的运行效率. 最后,该算法通过获取的人群运动矢量交点密集度及能量信息来识别人群异常行为. 对多个视频进行测试,测试结果表明,该算法能够以较高正确率识别视频中人群的异常行为,同时还有效提高了算法的运行速度.

    Abstract:

    In order to detect the abnormal crowd behavior in video surveillance in real time and more accurate, this study proposes a method of dynamic interframe space updating based on the Pyramid LK optical flow. The algorithm dynamically updates the interframe interval by extracting the crowd motion information, and then detects the crowd motion information at the interframe interval. In this way, the algorithm does not only preserve the advantages of the traditional algorithm in detecting crowd motion information, but also improves the efficiency. Finally, the algorithm identifies the abnormal crowd behavior by acquiring the intersection density and energy information of the crowd motion vector. By testing multiple videos, the test results show that the algorithm can identify the abnormal crowd behavior in the video with high accuracy, and also effectively improves the running speed.

    参考文献
    [1] Zhang X, Ding M, Fan GL. Video-based human walking estimation using joint gait and pose manifolds. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(7): 1540-1554. [DOI:10.1109/TCSVT.2016.2527218]
    [2] Yu TS, Wang RS. Enhancing scene parsing by transferring structures via efficient low-rank graph matching. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Burlingame, CA, USA. 2016. 22.
    [3] 候楠, 郭立君, 张荣. 基于集群运动局部有序性测度的异常行为检测. 数据通信, 2016, (1): 39-44.
    [4] 蔡瑞初, 谢伟浩, 郝志峰, 等. 基于多尺度时间递归神经网络的人群异常检测. 软件学报, 2015, 26(11): 2884-2896. [DOI:10.13328/j.cnki.jos.004893]
    [5] Lucas BD, Kanade T. An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence. Vancouver, BC, Canada. 1981. 674-679.
    [6] 蒋菱, 程赓. 基于LK光流跟踪法的有效目标点增强跟踪. 微型机与应用, 2015, 34(6): 45-49.
    [7] Ahn B, Han Y, Kweon IS. Real-time facial landmarks tracking using active shape model and LK optical flow. Proceedings of the 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). Daejeon, Korea. 2012. 541-543.
    [8] 兰红, 周伟, 齐彦丽. 动态背景下的稀疏光流目标提取与跟踪. 中国图象图形学报, 2016, 21(6): 771-780. [DOI:10.11834/jig.20160610]
    [9] Loy CC, Gong SG, Xiang T. From semi-supervised to transfer counting of crowds. Proceedings of 2013 IEEE International Conference on Computer Vision. Sydney, Australia. 2013. 2256-2263.
    [10] Wang Q, Ma Q, Luo CH, et al. Hybrid histogram of oriented optical flow for abnormal behavior detection in crowd scenes. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2): 1655007. [DOI:10.1142/S0218001416550077]
    [11] Rao AS, Gubbi J, Marusic S, et al. Crowd event detection on optical flow manifolds. IEEE Transactions on Cybernetics, 2016, 46(7): 1524-1537. [DOI:10.1109/TCYB.2015.2451136]
    [12] 钟帅, 蔡坚勇, 廖晓东, 等. 基于运动矢量交点密集度的人群恐慌行为检测. 计算机系统应用, 2017, 26(7): 210-214. [DOI:10.15888/j.cnki.csa.005871]
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

陈颖熙,廖晓东,钟帅.基于动态帧间间隔更新的人群异常行为检测.计算机系统应用,2018,27(2):207-211

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2017-05-22
  • 最后修改日期:2017-06-08
  • 在线发布日期: 2018-02-05
文章二维码
您是第11204427位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号