视频监控若干关键技术的初步研究
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摘要
视频监控在国民经济中发挥着越来越重要的作用，如何对监控系统中的图像序列进行实用化的智能场景检测分析和智能化管理，实现信息实时有效的获取和控制，是视频监控网络化、智能化的核心技术。
本论文在对视频监控技术的构成与发展进行研究分析的基础上，针对视频监控中涉及的异常检测、异常分析、智能查询和流式传输等若干关键技术进行了深入研究，并建立了视频监控系统的演示平台。
论文根据实际监控工程的需要，首先分析了安全监控中的异常情况，设计了静止物体缓慢变化时的监控算法、光线缓变情况下背景的更新策略、背景变化情况下的检测方法、光线缓慢变化情况下的检测以及消除噪声和干扰的算法等多种具有不同应用需求的算法，同时提出快速异常检测算法的评价公式。在对检测出的异常进行时间、坐标、大小、运动轨迹等分析和描述后，论文设计了基于异常图像内容描述的查询系统，建立了异常图像检索数据库，通过在压缩域提取图像特征节约了系统资源的开销，设计的“粗检+细检+分类”的三层细化检索过程提高了检索结果的准确性，友好的人机界面提供便利的异常查询。为满足视频监控中远程实时传输、处理和回放的需要，论文实现了一种局域网视频流传输技术，主要采用了Csocket套接字技术、DirectShow技术、多线程应用与同步处理技术。
通过对视频监控中图像序列的自动异常检测研究，建立基于内容的智能化查询系统，设计了实时流畅的视频流远程传输等。论文对改善监控系统性能、提升监控系统功能，提高视频监控系统的智能化水平和网络化程度作了有益的尝试。
Video surveillance is playing an increasingly important role in the national economy. How to perform intelligent scene detection and to realize real-time & effective management of information are the core technologies of video surveillance.
On the basis of analysis the structure and development of video surveillance technology, this paper researches on several key technologies involved in video surveillance, such as detection & analysis of anomaly, intelligent query, streaming transfer, etc., and complete the demonstration platform of video surveillance system.
According to the practical requirements of engineering, this paper designs various algorithms, such as those for monitoring objects experiencing a slow change, updating backgrounds while rays are changing slowly, detection under changing background, elimination noise. This paper also advances the formula for evaluating the validity of these algorithms. After providing the analysis of the time, coordinates, size, track and so on of these anomalies, this paper designs an query system based on content of anomalous image, which reduces spending on system resource by means of extracting characteristics of image in Compressed-Domain, improves the accuracy of detection results by the 3-tier detection process designed, i.e. "coarse detection + scrutinized detection + classification", and provide a friendly interface. For the purpose of satisfying the requirements of real-time transfer, processing and play back in the video surveillance, this paper implement a kind of intranet streaming video transfer technology, inc
luding Csocket and DirectShow technology, the multithreading application and synchronized processing technology as well.
Through researches on several key technologies involved in video surveillance, this paper carries out a helpful attempt to enhance functions of monitoring system and improve intelligent level of video surveillance system.
本论文在对视频监控技术的构成与发展进行研究分析的基础上，针对视频监控中涉及的异常检测、异常分析、智能查询和流式传输等若干关键技术进行了深入研究，并建立了视频监控系统的演示平台。
论文根据实际监控工程的需要，首先分析了安全监控中的异常情况，设计了静止物体缓慢变化时的监控算法、光线缓变情况下背景的更新策略、背景变化情况下的检测方法、光线缓慢变化情况下的检测以及消除噪声和干扰的算法等多种具有不同应用需求的算法，同时提出快速异常检测算法的评价公式。在对检测出的异常进行时间、坐标、大小、运动轨迹等分析和描述后，论文设计了基于异常图像内容描述的查询系统，建立了异常图像检索数据库，通过在压缩域提取图像特征节约了系统资源的开销，设计的“粗检+细检+分类”的三层细化检索过程提高了检索结果的准确性，友好的人机界面提供便利的异常查询。为满足视频监控中远程实时传输、处理和回放的需要，论文实现了一种局域网视频流传输技术，主要采用了Csocket套接字技术、DirectShow技术、多线程应用与同步处理技术。
通过对视频监控中图像序列的自动异常检测研究，建立基于内容的智能化查询系统，设计了实时流畅的视频流远程传输等。论文对改善监控系统性能、提升监控系统功能，提高视频监控系统的智能化水平和网络化程度作了有益的尝试。
Video surveillance is playing an increasingly important role in the national economy. How to perform intelligent scene detection and to realize real-time & effective management of information are the core technologies of video surveillance.
On the basis of analysis the structure and development of video surveillance technology, this paper researches on several key technologies involved in video surveillance, such as detection & analysis of anomaly, intelligent query, streaming transfer, etc., and complete the demonstration platform of video surveillance system.
According to the practical requirements of engineering, this paper designs various algorithms, such as those for monitoring objects experiencing a slow change, updating backgrounds while rays are changing slowly, detection under changing background, elimination noise. This paper also advances the formula for evaluating the validity of these algorithms. After providing the analysis of the time, coordinates, size, track and so on of these anomalies, this paper designs an query system based on content of anomalous image, which reduces spending on system resource by means of extracting characteristics of image in Compressed-Domain, improves the accuracy of detection results by the 3-tier detection process designed, i.e. "coarse detection + scrutinized detection + classification", and provide a friendly interface. For the purpose of satisfying the requirements of real-time transfer, processing and play back in the video surveillance, this paper implement a kind of intranet streaming video transfer technology, inc
luding Csocket and DirectShow technology, the multithreading application and synchronized processing technology as well.
Through researches on several key technologies involved in video surveillance, this paper carries out a helpful attempt to enhance functions of monitoring system and improve intelligent level of video surveillance system.
引文
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In: IEEE Computer Society Conference. Computer Vision and Pattern Recognition. 1999:319-325
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2 谭铁牛．智能视觉监控技术概述．第一届全国智能视觉监控学术会议．北京，2002，5月
3 M.Saptharishi, K.Bhat, C.Diehl etc. Recent Advances in Distributed Collaborative Surveillance. SPIE Proceedings on Unattended Ground Sensor Technologies and Applications (AeroSense 2000), vol.4040, April, 2000:199-208
4 Vinod Nair, James J. Clark. Automated Visual Surveillance Using Hidden Markov Models. The 15th Vision Interface Conference. Calgary, May 2002:88-92
5 Stauffer C, Grimson W.E.L. Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference. Computer Vision and Pattern Recognition. 1999:246-252
6 雷晓峰．远程智能图像监控系统的研究与开发．湖南大学硕士论文．2002，3
7 周兵．视频监控中的运动对象检测．北京航空航天大学博士论文．2003，5
8 岑峰，戚飞虎，陈茂林．长期视频监控系统的多分布模型背景差方法．红外与毫米波学报．2002，21(1)：59-63
9 卢强，陈泉林，奉玲，林康红．视频交通监控系统的开发．计算机应用．2001，21(12)：69-70
10 刘晓冬，苏光大，周全，田超．一种可视化智能户外监控系统．中国图象图形学报2000，5(A)(12)：1024-1029
11 Meyer M, Hotter M, Ohmacht T. A new system for video-based detection of moving objects and its integration into digital networks. In: Security Technology, 30th Annual 1996 International Carnahan Conference. 1996.105-110
12 lireza Behrad, Ali Shahrokni, Seyed Ahmad Motamedi and Kurosh Madani. A Robust Vision-based Moving Target Detection and Tracking System. In: Proceeding of Image and Vision Computing conference (IVCNZ2001).2001.
13 Walter Bell, Pedro Felzenszwalb, Dan Huttenlocher. Detection and long term tracking of moving object in aerial video. March 26th, 1999
14 Cohen I, Mcdioni G. Detecting and tracking moving objects for video surveillance.
In: IEEE Computer Society Conference. Computer Vision and Pattern Recognition. 1999:319-325
15 Paragios N.K, Deriche R. A PDE-based level-set approach for detection and tracking of moving objects. In: Sixth International Conference on Computer Vision. 1998:1139-1145
16 Odobez J.M, Bouthemy P. Detection of multiple moving objects using multiscale MRF with camera motion compensation. In: IEEE International Conference on Image Processing (ICIP-94). 1994:257-261
17 A. Lipton, H. Fujiyoshi, and R. Patil. Moving Target Classification and Tracking from Real-time Video. Proc. of the 1998 DARPA Image Understanding Workshop (IUW'98). Proc. of the Workshop on Application of Computer Vision, IEEE. October, 1998:8-14
18 Besson S.J, Barlaud M, Aubert G. Detection and tracking of moving objects using a new level set based method. In: 15th International Conference on Pattern Recognition. 2000, 1100-1105
19 韩军、熊璋、龚声蓉、孙文彦．基于运动对象自动检测和查询的监控系统．北京航空航天大学学报．2001，27(4)：400-403
20 乔予杰，胡俊，苏祥芳等．基于分级的MRF视频运动检测算法及其实现．武汉大学学报(理学版)．2002，48(1)：99-102
21 G. Gordon, T. Darrell, M.Harville etc. Background estimation and removal based on range and colon In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, CO. 1999,7
22 D.Gibbins, G.N.Newsam, M.J.Brooks. Detecting suspicious background changes in video surveillance of busy scenes. IEEE. 1996:22-26
23 Francesco Ziliani, Andrea Cavallaro. Image analysis for video surveillance based on spatial regularization of a statistical model-based change detection. Real-Time Imaging, Special Issue on: Video Processing and Communications in Real-Time Video-Based Surveillance Systems. 2001, 7(5): 389-399.
24 孙洪波，杨兆选，杨移山．实时动态图像检测系统研究．测控技术．1997，2
25 黄中文，戚飞虎，岑峰，陈茂林．大范围场景智能实时视频监控系统．第一届全国智能视觉监控学术会议．北京，2002，5：157-162
26 Michael Harville, Gaile Gordon, John Woodfill. Foreground Segmentation Using
Adaptive Mixture Models in Color and Depth. Proceedings of the IEEE Workshop on Detection and Recognition of Events in Video. Vancouver, Canada, July 2001.
27 A.Mitiche, R.Feghali, A.Mansouri. Tracking moving objects as spatio-temporal boundary detection. Fifth IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI'02).2002
28 Michael Meyer, Michael Hotter, Thorsten Ohmacht. New options in video surveillance applications using multiple views of a scene. 1998, IEEE: 216-219
29 A K Benkhalil, S S Ipson, W Booth. Real-time detection and tracking of a moving object using a complex programmable logic device. 1998 The Institution of Electrical Engineers. IEEE, London, WC2R OBL, UK.
30 Chieh-Chih Wang, Chuck Thorpe. Simultaneous localization and mapping with detection and tracking of moving objects. Proceedings of the 2002 IEEE International Conference on Robotics & Automation. Washington,DC. 2002: 2918-2924
31 W.E.L Grimson, Chris Staufer, Raquel Romano etc. Using adaptive tracking to classify and monitor activities in a site. In- IEEE Conf. Computer Vision and Pattern Recognition. 1998:22-29
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