基于加权HU不变矩的监控视频人体行为识别方法的研究与实现
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摘要
随着视频监控系统在社区安防、机场安检、景区安全等方面的广泛应用,如何实现快速、精确的异常行为检测成为视频监控中一个亟待解决的问题,同时也成为近年来计算机视觉领域的一个研究热点。本文研究并改进了运动人体行为识别处理中的关键算法,主要工作进展包括：
(1)在图形图像处理方面,针对监控原始图像模糊粗糙问题,本文采用对视频监控图像实施平滑和滤波处理,获得了清晰准确的改进图像。
(2)在运动目标检测方面,本文提出背景减除法与时间差分法相结合的加权平均法。为保证检测的高效性、准确性,针对背景变化简单的情况,本文提出基于背景更新与自适应阈值选取相结合的方法,提高算法性能；针对背景复杂情况,本文提出基于高斯混合背景模型的运动检测方法,它根据实验结果分析动态调整混合高斯模型参数,有效解决光照以及树木扰动等外界变化带来的影响。
(3)在运动目标行为识别方面,针对要提高人体异常行为识别效率,提出了一种以改进的加权Hu不变矩作为目标特征,利用滑窗机制对连续视频帧的匹配结果进行综合分析的方法,从而实现对人体异常行为的识别；同时研究了基于目标轮廓特征和基于隐马尔科夫模型的识别算法,以目标的最小外接矩形的长宽比、目标的覆盖度以及目标的轮廓特征组成的特征向量作为人体行为的特征描述,判别人体行为是否异常。
本文设计实现了一个针对人体运动行为识别的智能视频监控系统,验证了改进的算法可有效检测出实时视频中运动的目标人体,并识别出其行为的变化。
With the extensive use of video surveillance systems in Community security, safety and other aspects, How to achieve rapid,accurate detection of abnormal behavior become video monitoring a problem to be solved, but also become a hot research topic in the field of computer vision. This paper study and improved the movement Human behavior in the processing of identification key algorithms, mainly including work progress:
(1)As to the image blurring rough for monitoring, before graphic image processing, we choose the methods of smoothing, filtering and other pre-processing, and then the image has not been destroyed.
(2)In moving target detection, change the background image for the inconsistency, while ensuring the efficient simplicity, this paper presents a simple change in the background of the case that the problem of the shadow, and select updating and threshold selection combined to improve performance of the algorithm based on adaptive background; in the context of complex situations, we choose the Gaussian mixture model-based extraction of moving targets, through the analysis of test results, we improve the Gaussian mixture model parameters, and solve the light and the trees outside disturbances such as the impact of changes. Finally, a fast YUV color space shadow suppression algorithm.
(3) In identifying the target behavior in the campaign, in order to make the efficiency for identifying abnormal behavior more convenient, we improve the weighted Hu invariant moments as the target, the use of sliding window mechanism to match the results of successive video frames a comprehensive analysis to human behavior identification; while the target contour based features and hidden Markov model-based algorithm for the minimum bounding rectangle of the target aspect ratio, target coverage and target contour feature vector consisting of features described as characteristic of human behavior, the human behavior on the basis of discrimination is abnormal.
This design implements a video surveillance system of motion human identification. we show that:the proposed method can effectively detect moving targets in real-time video and identify the movement of human behavior.
(1)在图形图像处理方面,针对监控原始图像模糊粗糙问题,本文采用对视频监控图像实施平滑和滤波处理,获得了清晰准确的改进图像。
(2)在运动目标检测方面,本文提出背景减除法与时间差分法相结合的加权平均法。为保证检测的高效性、准确性,针对背景变化简单的情况,本文提出基于背景更新与自适应阈值选取相结合的方法,提高算法性能；针对背景复杂情况,本文提出基于高斯混合背景模型的运动检测方法,它根据实验结果分析动态调整混合高斯模型参数,有效解决光照以及树木扰动等外界变化带来的影响。
(3)在运动目标行为识别方面,针对要提高人体异常行为识别效率,提出了一种以改进的加权Hu不变矩作为目标特征,利用滑窗机制对连续视频帧的匹配结果进行综合分析的方法,从而实现对人体异常行为的识别；同时研究了基于目标轮廓特征和基于隐马尔科夫模型的识别算法,以目标的最小外接矩形的长宽比、目标的覆盖度以及目标的轮廓特征组成的特征向量作为人体行为的特征描述,判别人体行为是否异常。
本文设计实现了一个针对人体运动行为识别的智能视频监控系统,验证了改进的算法可有效检测出实时视频中运动的目标人体,并识别出其行为的变化。
With the extensive use of video surveillance systems in Community security, safety and other aspects, How to achieve rapid,accurate detection of abnormal behavior become video monitoring a problem to be solved, but also become a hot research topic in the field of computer vision. This paper study and improved the movement Human behavior in the processing of identification key algorithms, mainly including work progress:
(1)As to the image blurring rough for monitoring, before graphic image processing, we choose the methods of smoothing, filtering and other pre-processing, and then the image has not been destroyed.
(2)In moving target detection, change the background image for the inconsistency, while ensuring the efficient simplicity, this paper presents a simple change in the background of the case that the problem of the shadow, and select updating and threshold selection combined to improve performance of the algorithm based on adaptive background; in the context of complex situations, we choose the Gaussian mixture model-based extraction of moving targets, through the analysis of test results, we improve the Gaussian mixture model parameters, and solve the light and the trees outside disturbances such as the impact of changes. Finally, a fast YUV color space shadow suppression algorithm.
(3) In identifying the target behavior in the campaign, in order to make the efficiency for identifying abnormal behavior more convenient, we improve the weighted Hu invariant moments as the target, the use of sliding window mechanism to match the results of successive video frames a comprehensive analysis to human behavior identification; while the target contour based features and hidden Markov model-based algorithm for the minimum bounding rectangle of the target aspect ratio, target coverage and target contour feature vector consisting of features described as characteristic of human behavior, the human behavior on the basis of discrimination is abnormal.
This design implements a video surveillance system of motion human identification. we show that:the proposed method can effectively detect moving targets in real-time video and identify the movement of human behavior.
引文
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[2]贾云得.机器视觉.北京：科学出版社,2004
[3]丁明跃等.不变矩算法研究.数据采集与处理,1992,3,7(1)：1-9
[4]Yang J and Waibel A. A real-time face tracker. In:Proc IEEE Workshop on Applications of Computer Vision, Sarasota, USA,1996:142-147
[5]Elgammal A, Duralswami R, Harwood D. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 2002,90(7):1153-1163
[6]印勇等.基于改进Hu矩的异常行为识别.计算机技术与发展,2009,9.19(9)：90-92
[7]王冰,张仲选.基于差分法的灰度图像矩的快速算法.计算机应用与软件,2005,11
[8]G.Johansson. Visual motion perception Scientific American.76-88 November 1976
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[12]范勇,游志胜,张建州等.一种快速运动目标检测与跟踪算法.光电工程,2000,27(6)
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[14]P. Kaew TrakulPong.R. Bowden. An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection. Proc.2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01.2001.
[15]Cutler R and Davis L. Robust real-time periodic motion detection, analysis, and applications. IEEE Trans Pattern Analysis and Machine Intelligence,2000,22 (8): 781-796
[16]Sharma R. Pavlovic V. Huang T. Toward multi model human-computer interface. In: Proc IEEE Special Issue on Multimedia Signal Processing.1998.86 (5):853-869
[17]L. Yang and F. Algregtsen, "Fast computation of invariant geimetric moments:A new method giving correct results," Proc. IEEE Int. Conf. On Image Processing,1994
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[22]H. G. Barrow. Parametric correspondence and chamfer matching:Two new techniques for image matching. In Proc.5th Int. Joint Conf. Artificial Intelligence,1997
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[32]付晓薇,方康玲,李曦.一种基于特征的多目标跟踪算法.计算机工程与应用, 2004(35)
[33]P. Perez, J. Vermaak and A. Blake. Data fusion for visual tracking with particles. Proceedings of the IEEE,2004,92(3):495-513
[34]J.H.Cho,S.D.Kim,Object detection using spatio-temporal thresholding in image sequences, ELECTRONICS LETTERS 2nd,2004:22-23
[35]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,3(25)：1-16
[36]施智平,胡宏,李清勇.一种快速有效的图像纹理谱描述子.计算机辅助设计与图形学学报，2004.12
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[39]Polana R and Nelson R. Low level recognition of human motion. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, TX,1994:77-82
[40]M.Isard.A.Blake.Condensation-conditional density propagation for visual tracking, International Journal of Computer Vision,1998,29(1):5-28
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[43]I. Tierney. A. Mira. Some adaptive monte carlo methods for bayesian inference. Statistics in Medicine.1999,(18):2507-2015
[44]Starner T and Pentland A. Real-time American Sign Language recognition from video using hidden Markov models. In:Proc International Symposium on Computer Vision, Coral Gables, Florida,1995:265-270
[45]T. Ojala. M. Pietikainen and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987
[46]P.Huttenlocker and W. Rucklidge, Tracking nonrigid objects in complex scenes. Proc. Int. Conf. Computer Vision (ICCV), Sept.1992.
[47]J.Chen. Charles A. Bouman, John C. Dalton. Similarity Pyramids for Browsing and Organization of Large Image Database[C], In Proc. SPIE/IE&I Conf. Human Vision and Electronic Imaging Ⅲ, Vol.3299, San Jose, CA, Jan,1998
[48]Bregler C. Learning and recognizing human dynamics in video sequences. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997:568-574
[49]J.K.Aggarwal, Q.Cai. Human Motion Analysis:A Review. Computer Vision and Image Understanding,1999.73(3):428-440
[50]LI Y. Reforming the theory of invariant moments for pattern recognition[J]. Pattern Recognition.1992.25(7):723-731
[51]H. Wang, D. Suter and K. Schindler. Effective appearance model and similarity measure forparticle filtering and visual tracking. In:Proceedings of 9th European Conference on Computer Vision, Graz, Austria,2006, (3):606-618
[52]Wang and M. Brady, Real-time corner detection algorithm for motion estimation. Image Vision Comput,vol.13:695-703,Nov.1995
[53]Collins R. Lipton A and Kanade T. Introduction to the special section on video surveillance. IEEE Trans Pattern Analysis and Machine Intelligence.2000.22 (8): 745-746
[54]Yang J. Xu Y. Chen CS. Human action learning via hidden Markov model. IEEE Transactions on Systems Man and Cybernetics,1997,27(1):34-44
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[2]贾云得.机器视觉.北京：科学出版社,2004
[3]丁明跃等.不变矩算法研究.数据采集与处理,1992,3,7(1)：1-9
[4]Yang J and Waibel A. A real-time face tracker. In:Proc IEEE Workshop on Applications of Computer Vision, Sarasota, USA,1996:142-147
[5]Elgammal A, Duralswami R, Harwood D. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 2002,90(7):1153-1163
[6]印勇等.基于改进Hu矩的异常行为识别.计算机技术与发展,2009,9.19(9)：90-92
[7]王冰,张仲选.基于差分法的灰度图像矩的快速算法.计算机应用与软件,2005,11
[8]G.Johansson. Visual motion perception Scientific American.76-88 November 1976
[9]M.Flickner.H.Hafner. "Query by image and video content:The QBIC system". IEEE Computer. Sept,1995. Vol.28,No.9:23-32
[10]王晓卫,宁固.一种改进的基于光流的运动目标的检测算法[J].武汉大学学报,信息科学版,2003,3(28)：352-353
[11]Guo Y,.Xu G and Tsuji S. Understanding human motion patterns. In:Proc International Conference on Pattern Recognition, Jerusalem, Israel,1994:325-329
[12]范勇,游志胜,张建州等.一种快速运动目标检测与跟踪算法.光电工程,2000,27(6)
[13]Du S P,Li H. The second order HMM and the applications in language computing [J] Vol.41,No.2,2004
[14]P. Kaew TrakulPong.R. Bowden. An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection. Proc.2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01.2001.
[15]Cutler R and Davis L. Robust real-time periodic motion detection, analysis, and applications. IEEE Trans Pattern Analysis and Machine Intelligence,2000,22 (8): 781-796
[16]Sharma R. Pavlovic V. Huang T. Toward multi model human-computer interface. In: Proc IEEE Special Issue on Multimedia Signal Processing.1998.86 (5):853-869
[17]L. Yang and F. Algregtsen, "Fast computation of invariant geimetric moments:A new method giving correct results," Proc. IEEE Int. Conf. On Image Processing,1994
[18]王典,程咏梅,杨涛等.基于混合高斯模型的运动阴影抑制算法Computer Applications.2006. Vol.26, No.5
[19]Stringa E. Morphological change detection algorithms for surveillance applications. In: British Machine Vision Conference. Bristol. UK.2000:402-411
[20]DING GG. GUO BL. New Fast Motion Estimation Algorithm Based on Line Search [J]. Journal of xipan JiaoTong University.2004.38(2):36-39
[21]Li Jin. Research on real-time detection of moving target using gradient optical flow. IEEE International Conference on Mechatronics and Automation.2005:1796-1801
[22]H. G. Barrow. Parametric correspondence and chamfer matching:Two new techniques for image matching. In Proc.5th Int. Joint Conf. Artificial Intelligence,1997
[23]Otsu N. A Threshold selection Method from Gray-level Histograms. IEEE Transaction on Systems. Man and Cybernetics.1979. SMC-9(1):62-66
[24]S. T. Birchfield and S. Rangarajan. Spatiograms versus histograms for region-based tracking. In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego. CA. United States.2005.2:1158-1163
[25]Wren C. Azarbayejani A. Darrell T and Pentland A. Pfinder:real-time tracking of the human body. IEEE Trans on Pattern Analysis and Machine Intelligence.1997.19 (7): 780-785
[26]Pentland. Machine understanding of human action. In Proc.7th Int. Forum Frontier of Telecommunication Technology, Tokyo. Nov.1995
[27]F Mayer. S Beucher. Morphologysegmentation[J].J.Visual Comm. And Image Representation.1990.1 (1):21-46
[28]万华林,Chowdhury Morshed U,胡宏等.图像纹理特征及其在CBIR中的应用.计算机辅助设计与图形学学报,2003,15(2)：195-199
[29]C. Shen, A. van den Hengel and A. Dick. Probabilistic multiple cue integration for particle filter based tracking. In:International Conference on Digital Image Computing: Techniques and Applications, Sydney,2003,1:399-408
[30]Bhaskar Harish, Mihaylova Lyudmila. Automatic object detection based on adaptive background subtraction using symmetric alpha stable distribution. IET Seminar Digest.2008,12(27):195-203
[31]Isard M and Blake A. Contour tracking by stochastic propagation of conditional density. In:Proc European Conference on Computer Vision, Cambridge,1996:343-356
[32]付晓薇,方康玲,李曦.一种基于特征的多目标跟踪算法.计算机工程与应用, 2004(35)
[33]P. Perez, J. Vermaak and A. Blake. Data fusion for visual tracking with particles. Proceedings of the IEEE,2004,92(3):495-513
[34]J.H.Cho,S.D.Kim,Object detection using spatio-temporal thresholding in image sequences, ELECTRONICS LETTERS 2nd,2004:22-23
[35]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,3(25)：1-16
[36]施智平,胡宏,李清勇.一种快速有效的图像纹理谱描述子.计算机辅助设计与图形学学报，2004.12
[37]Cui Y and Weng J. Hand segmentation using learning-based prediction and verification for hand sign recognition. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997:88-93
[38]C. Yang. R. Duraiswami and L. Davis. Fast multiple object tracking via a hierarchical particle filter. In:Proceedings of IEEE International Conference on Computer Vision, Beijing, China,2005,1:212-219
[39]Polana R and Nelson R. Low level recognition of human motion. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, TX,1994:77-82
[40]M.Isard.A.Blake.Condensation-conditional density propagation for visual tracking, International Journal of Computer Vision,1998,29(1):5-28
[41]孔恩克(美),施伯乐等译.数据库处理基础.设计与实现.北京,电子工业出版社,1998
[42]Bobick A and Davis J. Real-time recognition of activity using temporal templates. In: Proc IEEE Workshop on Applications of Computer Vision, Sarasota, Florida,1996: 39-42
[43]I. Tierney. A. Mira. Some adaptive monte carlo methods for bayesian inference. Statistics in Medicine.1999,(18):2507-2015
[44]Starner T and Pentland A. Real-time American Sign Language recognition from video using hidden Markov models. In:Proc International Symposium on Computer Vision, Coral Gables, Florida,1995:265-270
[45]T. Ojala. M. Pietikainen and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987
[46]P.Huttenlocker and W. Rucklidge, Tracking nonrigid objects in complex scenes. Proc. Int. Conf. Computer Vision (ICCV), Sept.1992.
[47]J.Chen. Charles A. Bouman, John C. Dalton. Similarity Pyramids for Browsing and Organization of Large Image Database[C], In Proc. SPIE/IE&I Conf. Human Vision and Electronic Imaging Ⅲ, Vol.3299, San Jose, CA, Jan,1998
[48]Bregler C. Learning and recognizing human dynamics in video sequences. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997:568-574
[49]J.K.Aggarwal, Q.Cai. Human Motion Analysis:A Review. Computer Vision and Image Understanding,1999.73(3):428-440
[50]LI Y. Reforming the theory of invariant moments for pattern recognition[J]. Pattern Recognition.1992.25(7):723-731
[51]H. Wang, D. Suter and K. Schindler. Effective appearance model and similarity measure forparticle filtering and visual tracking. In:Proceedings of 9th European Conference on Computer Vision, Graz, Austria,2006, (3):606-618
[52]Wang and M. Brady, Real-time corner detection algorithm for motion estimation. Image Vision Comput,vol.13:695-703,Nov.1995
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