[1]张丽坤,孙建德,李静.视觉关注转移的事件检测算法[J].智能系统学报,2012,7(04):333-338.
 ZHANG Likun,SUN Jiande,LI Jing.Event detection based on visual attention shift[J].CAAI Transactions on Intelligent Systems,2012,7(04):333-338.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年04期
页码:
333-338
栏目:
出版日期:
2012-08-25

文章信息/Info

Title:
Event detection based on visual attention shift
文章编号:
1673-4785(2012)04-0333-06
作者:
张丽坤1 孙建德1 李静2
1.山东大学 信息科学与工程学院, 山东 济南250100;
2.山东省工会管理干部学院 信息工程学院, 山东 济南250100
Author(s):
ZHANG Likun1 SUN Jiande1 LI Jing2
1.School of Information Science and Engineering, Shandong University, Ji’nan 250100, China;
2. School of Information Engineering, Shandong Institute of Trade Unions’ Administration Cadres, Ji’nan 250100, China
关键词:
〗智能监控事件检测事件发生视觉关注模型视觉关注节奏关键帧提取
Keywords:
intelligent surveillance event detection occurrence of event visual attention model visual attention rhythm key frame extraction
分类号:
TP18;TN911.73
文献标志码:
A
摘要:
智能监控系统已广泛应用于银行、超市、公交车等公共场合,监控视频的事件检测已经成为智能监控中的关键技术.提出了一种基于视觉关注转移的事件检测方法,该方法首先分别通过对视频帧进行动态和静态受关注模型的提取得到视觉关注显著图,然后根据视觉关注显著图的时域特性形成视觉关注节奏曲线,根据视觉关注节奏的变化强度选取关键帧,以关键帧形式表示受关注事件的发生.实验结果表明,算法提取的关键帧可以准确地标示监控视频中特征事件的发生,并且可以做到实时地检测事件.
Abstract:
Intelligent surveillance systems have been widely used in banks, supermarkets, buses, and other public places. Event detection in a surveillance video is a key technology for this field. In this paper, a visual attention shiftbased event detection algorithm was proposed for intelligent surveillance in which the dynamic and static visual attention regions were detected to obtain the visual saliency map. After that, the visual attention rhythm was derived from the visual saliency map temporally. According to the visual attention rhythm, the key frames were selected to label the occurrence of the events. Experimental results demonstrate that the proposed algorithm can label the occurrence of the events with the extracted key frames correctly, and that the event detection is performed in realtime.

参考文献/References:

[1]JIANG F, WU Y. A dynamic hierarchical clustering method for trajectorybased unusual video event detection[J]. IEEE Transactions on Image Processing, 2009, 18(4): 907913.
[2]LIU C, WANG G J, NING W X, et al. Anomaly detection in surveillance video using motion direction statistics[C]// IEEE International Conference on Image Processing. Hong Kong, China, 2010: 717720.
[3]JIANG P, QIN X L. Keyframebased video summary using visual attention clues[J].IEEE Transactions on Multimedia, 2010, 17(2): 6473.
[4]LAI J L, YI Y. Key frame extraction based on visual attention model[J]. Journal of Visual Communication and Image Representation, 2012, 23(1): 114125. 
[5]AMIRI A, FATHY M, NASERI A. Keyframe extraction and video summarization using QRdecomposition[C]//IEEE International Conference on Multimedia Technology and Applications. Wuhan, China, 2010: 134139.
[6]ZHANG J, SUN J D, YAN H, et al. Visual attention model with crosslayer saliency optimization[C]//IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Dalian, China, 2011: 240243.
[7]ITTI L, KOCH C, NIEBUR E. A model of saliency based visual attention for rapid scene analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 12541259.
[8]RUBINSTEIN M, SHAMIR A, AVIDAN S. Multioperator media retargeting[J].ACM Transactions on Graphics, 2009, 23(3): 18. 

相似文献/References:

[1]毋 非,封化民,申晓晔.容错粗糙模型的事件检测研究[J].智能系统学报,2009,4(02):112.
 WU Fei,FENG Hua-min,SHEN Xiao-ye.Research on event detection based on the tolerance rough set model[J].CAAI Transactions on Intelligent Systems,2009,4(04):112.

备注/Memo

备注/Memo:
收稿日期:2012-03-02.
网络出版日期:2012-07-12.
基金项目:国家自然科学基金资助项目(61001180).
通信作者:孙建德.
E-mail:jd_sun@sdu.edu.cn.
作者简介:
张丽坤,女,1986年生,硕士研究生,主要研究方向为多媒体信号处理.
孙建德,男,1978年生,副教授.主要研究方向为基于内容的多媒体分析、基于视觉关注模型的图像/视频分析、图像/视频复制检测、多媒体信息内容安全和数字水印等.承担及完成科研项目10余项,授权发明专利8项,发表学术论文40余篇.
李静,女,1979年生,主要研究方向为多媒体通信.
更新日期/Last Update: 2012-09-27