基于混合高斯模型的运动检测及阴影消除算法研究
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摘要
运动检测是数字图像处理技术的重要研究内容之一,也是当前运动视觉研究中尚未根本解决的难点问题。自适应混合高斯模型算法作为一种经典的运动检测算法已经得到了广泛的应用,但是该算法的模型更新策略存在不足；同时运动阴影的存在会干扰运动目标的精确提取,为了减少阴影对运动目标提取的干扰,人们针对阴影具备的特性,提出了很多阴影消除方法,但这些阴影消除算法一般都会受到应用场景的限制。本文的研究目的是改善混合高斯算法的运动检测效果,扩展阴影消除算法的适用场景,主要工作如下：
针对传统混合高斯背景模型算法采用统一更新速率及高斯分布方差过度收敛引起的误检测问题,本文提出一种新的模型更新策略。改进算法在对背景模型进行更新时,首先根据模型信息和前景检测结果动态地区分出背景的多模态区域、运动区域、背景逃离区域等,然后对不同的区域采用不同的更新速率；此外,本文在前景判定时引入一种阈值检测方法,以更好地应对由于背景扰动引起的误检测。
针对基于单种图像特征的阴影检测算法容易受到场景的限制,以及光照变化引起的误检测问题,本文提出了一种融合HSV颜色特征和图像梯度特征的阴影检测算法。算法首先根据阴影区域和背景的亮度差异以及色调和饱和度的不变性进行阴影判断,然后利用阴影区域的梯度恒定性进行阴影检测,最后综合这两种阴影判定结果得到最终的阴影检测结果；此外,在基于HSV颜色特征的阴影判定规则中加入了一项判断条件,以解决当光照强度由弱变强而导致的误检测。
为了验证算法的有效性,本文在PC机上借助VS2005、OpenCV编程工具实现了本文算法及相关对比算法,并采用来源于美国加州大学计算机视觉和机器人研究实验室网站的监控视频素材,对算法进行测试评估。实验结果表明：改进后的混合高斯算法能够更好地处理背景中的多模态区域,提高运动目标分割精度；融合的阴影检测算法在不同场景下取得了较好的阴影抑制效果,并在引入阴影消除算法之后,降低了由于光照突变引起的误检测。
Motion detection is one of important researches in digital image processing and is also the difficult problem which need to be solved in current motion vision research. As a classic algorithm of the motion detection, Adaptive Gaussian Mixture Model algorithm has been widely used, but its update strategy exists some deficiencies; In the same time, moving shadow would interfere the accurate extraction of moving targets, In order to reduce this interference, researchers has proposed a lot of shadow elimination method according to the features of shadow. However, these algorithms are generally limited by the application scenarios. The purpose of this paper is to improve the effects of motion detection based on Gaussian Mixture algorithm and extend the scenes to which shadow elimination algorithm applies, main tasks of this paper are as follows:
Because traditional Gaussian Mixture Background Model algorithm uses uniform update rate and the Gaussian variance over convergence lead to the error detection. In this paper, we proposed a new update strategy. When updating the background model, the improved algorithm first divided the background into multi-modal regions, moving regions and escaping regions in dynamic, according to the model information and the result of prospect detection. Then, we assign different update rates for the different regions; Besides, when in the process of prospect detection we are introducing a threshold detection method, which could process error detection caused by illumination changes better.
Shadow detection algorithm based on one kind of image feature is easily limited by the scene and illumination changes, which will leads to error detection. In this paper, a shadow detection algorithm based on HSV color feature and image Gradient feature is proposed. Firstly, the shadow is determined by the brightness differences between the shadow region and the background, the constancy of chroma and saturation. Then, the shadow is detected according to the Gradient invariance, Finally, the final results is gotten by combining these two results of shadow determination; Besides, we added a determine condition to the shadow decision rules based on HSV color features, which could solve the error detection caused by light intensity from weak to strong.
To verify the effectiveness of the algorithm, with the programming tools of VS2005 and OpenCV, this paper achieved our algorithm and relevant comparison algorithm and applied these algorithms on the surveillance video material downloaded from the Computer Vision and Robotics Research Laboratory, University of California. The results show that:The Improved Gaussian Mixture algorithm could better handle the multi-modal regions and improve the precision of moving object segmentation; The fusion algorithm for shadow detection could achieve good inhibitory effect of the shadow and reduce the error detection caused by light mutation after introduction shadow elimination algorithm.
针对传统混合高斯背景模型算法采用统一更新速率及高斯分布方差过度收敛引起的误检测问题,本文提出一种新的模型更新策略。改进算法在对背景模型进行更新时,首先根据模型信息和前景检测结果动态地区分出背景的多模态区域、运动区域、背景逃离区域等,然后对不同的区域采用不同的更新速率；此外,本文在前景判定时引入一种阈值检测方法,以更好地应对由于背景扰动引起的误检测。
针对基于单种图像特征的阴影检测算法容易受到场景的限制,以及光照变化引起的误检测问题,本文提出了一种融合HSV颜色特征和图像梯度特征的阴影检测算法。算法首先根据阴影区域和背景的亮度差异以及色调和饱和度的不变性进行阴影判断,然后利用阴影区域的梯度恒定性进行阴影检测,最后综合这两种阴影判定结果得到最终的阴影检测结果；此外,在基于HSV颜色特征的阴影判定规则中加入了一项判断条件,以解决当光照强度由弱变强而导致的误检测。
为了验证算法的有效性,本文在PC机上借助VS2005、OpenCV编程工具实现了本文算法及相关对比算法,并采用来源于美国加州大学计算机视觉和机器人研究实验室网站的监控视频素材,对算法进行测试评估。实验结果表明：改进后的混合高斯算法能够更好地处理背景中的多模态区域,提高运动目标分割精度；融合的阴影检测算法在不同场景下取得了较好的阴影抑制效果,并在引入阴影消除算法之后,降低了由于光照突变引起的误检测。
Motion detection is one of important researches in digital image processing and is also the difficult problem which need to be solved in current motion vision research. As a classic algorithm of the motion detection, Adaptive Gaussian Mixture Model algorithm has been widely used, but its update strategy exists some deficiencies; In the same time, moving shadow would interfere the accurate extraction of moving targets, In order to reduce this interference, researchers has proposed a lot of shadow elimination method according to the features of shadow. However, these algorithms are generally limited by the application scenarios. The purpose of this paper is to improve the effects of motion detection based on Gaussian Mixture algorithm and extend the scenes to which shadow elimination algorithm applies, main tasks of this paper are as follows:
Because traditional Gaussian Mixture Background Model algorithm uses uniform update rate and the Gaussian variance over convergence lead to the error detection. In this paper, we proposed a new update strategy. When updating the background model, the improved algorithm first divided the background into multi-modal regions, moving regions and escaping regions in dynamic, according to the model information and the result of prospect detection. Then, we assign different update rates for the different regions; Besides, when in the process of prospect detection we are introducing a threshold detection method, which could process error detection caused by illumination changes better.
Shadow detection algorithm based on one kind of image feature is easily limited by the scene and illumination changes, which will leads to error detection. In this paper, a shadow detection algorithm based on HSV color feature and image Gradient feature is proposed. Firstly, the shadow is determined by the brightness differences between the shadow region and the background, the constancy of chroma and saturation. Then, the shadow is detected according to the Gradient invariance, Finally, the final results is gotten by combining these two results of shadow determination; Besides, we added a determine condition to the shadow decision rules based on HSV color features, which could solve the error detection caused by light intensity from weak to strong.
To verify the effectiveness of the algorithm, with the programming tools of VS2005 and OpenCV, this paper achieved our algorithm and relevant comparison algorithm and applied these algorithms on the surveillance video material downloaded from the Computer Vision and Robotics Research Laboratory, University of California. The results show that:The Improved Gaussian Mixture algorithm could better handle the multi-modal regions and improve the precision of moving object segmentation; The fusion algorithm for shadow detection could achieve good inhibitory effect of the shadow and reduce the error detection caused by light mutation after introduction shadow elimination algorithm.
引文
[1]李波. “863”数字视频监控系统：多一双眼睛看世界.www.people.com.cn, 2001-10-30
[2]中国研究报告网.2009-2012年视频监控系统行业发展前景分析及投资风险预测报告.www.a8m.cn,2009-10-28
[3]杜友田,陈峰,徐文立等.基于视觉的人的运动识别综述.电子学报,2007,35(1)：84-90
[4]Collins R T, Lipton A J, Kanade T, et al. A System for Video Surveillance and Monitoring. In:Proc of American Nuclear Society (ANS) Eighth International Topical Meeting on Robotics and Remote Systems. Carnegie Mellon,2000,25-29
[5]HID. Human Identification at a Distance. cc.gatech.edu/cpl/projects/hid/,2000
[6]VACE. Video Analysis and Content Extraction for Defense Intelligence. www.informedia.cs.cmu.edu,2003
[7]Haritaoglu I, Harwood D, Davis L S. W4:real-time surveillance of people and their activities. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000,22(8):809-830
[8]Remagnino P, Tan T, Baker K. Multi-agent visual sruveillance of dynamic scenes. Image and Vision Computing,1998,16(8):529-532
[9]Naylor M, Attwood C I. Final Report IST 1999-11287:ADVISOR. European:The ADVISOR Consortium,2003
[10].Matsuyama T. Cooperative Distributed Vision:Dynamic Integration of Visual Perception, Action, and Communication. In:Proc of The 23rd Annual German Conference on Artificial Intelligence:Advances in Artificial Intelligence. Bonn, 1999,75-88
[11]Renno J P, Makris D, Jones G A. Object Classification in Visual Surveillance Using Adaboost. In:Proc of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis,2007,1-8
[12]Zhou H, Yuan Y, Shi C M. Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding,2009,113(3):345-352
[13]Ullman S. Object recognition and segmentation by a fragment-based hierarchy. Trends in Cognitive Sciences,2007, 11(2):58-64
[14]代科学,李国辉,涂丹等.监控视频运动目标检测减背景技术的研究现状和展望.中国图象图形学报,2006,11(7)：919-927
[15]Horn B K P, Schunck B G. Determining optical flow. Artificial Intelligence, 1981,17(1-3):185-203
[16]施家栋,王建中,王红茹.基于光流的人体运动实时检测方法.北京理工大学学报,2008,28(9)：794-797
[17]郑锦,李波.视频序列中运动对象检测技术的研究现状与展望.计算机应用研究,2008,25(12)：3534-3540
[18]Neri A, Colonnese S, Russo G, et al. Automatic moving object and background separation. Signal Processing,1998,66(2):219-232
[19]Lipton A J, Fujiyoshi H, Patil R S. Moving target classification and tracking from real-time video. In:Proc of The Fourth IEEE Workshop on Applications of Computer Vision. New Jersey,1998,8-14.
[20]Piccardi M. Background subtraction techniques:a review. In:Proc of IEEE Conference on Systems, Man and Cybernetics. Hague,2004,3099-3104
[21]Hu J S, Su T M. Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance. EURASIP Journal on Advances in Signal Processing,2007,2007:1-14
[22]Chen Y T, Chen C S, Huang C R, et al. Efficient hierarchical method for background subtraction. Pattern Recognition,2007,40(10):2706-2715
[23]Hou Z Q, Han C Z. A background reconstruction algorithm based on pixel intensity classification in remote video surveillance System. In:Proc of the 7th International Conference on Information Fusion. Stockholm,2004,754-759
[24]Cucchiara R, Grana C, Piccardi M. Detecting Moving Objects, Ghosts and Shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1337-1342
[25]Toyama K, Krumm J, Brumitt B, et al. Wallfower:Principles and Practice of Background Maintenance. In:Proc of the 7th IEEE International Conference on Computer Vision. Kerkyra,1999,255-261
[26]Karmann K P, Brandt A. Moving Object Recognition Using an Adaptive Background Memory. In:Proc of Time Varying Image Processing and Moving Object Recognition. Cappellini,1990,289-296
[27]Ridder C, Munkelt O, Kirchner H. Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. In:Proc of the International Conference on Recent Advances in Mechatronics. Instanbul,1995,193:199
[28]Oliver N, Rosario B, Pentland A. A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):831-843
[29]Stauffer C, Grimson W E L. Adaptive background mixture models for real—time tracking. In:Proc of IEEE Conference on Computer Vision and Pattern Recognition. Colorado,1999,246-252
[30]Stauffer C, Grimson W E L. Learning Patterns of Activity Using Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, 22(8):747-757
[31]Heikkila J, Silve 0. A Real-Time System for Monitoring of Cyclists and Pedestrians. Image and Vision Computing,2004,22(7):563-570
[32]Salvador E, Cavallaro A, Ebrahimi T. Shadow identification and classification using invariant color models. Computer vision and image understanding,2004, 95(2):238-259
[33]Prati A, Mikic I, Trivedi M M. Detecting Moving Shadows:Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(7):918-923
[34]Wern C R, Azarbaycjani A, Darrell T, et al. Pfinder:Real time tracking of human body. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997, 19(7):780-785
[35]KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. In:Proc of the Second European Workshop on Advanced Video Based Surveillance Systems. Kingston,2001, 149-158
[36]Horprasert T, Harwood D, Davis L S. A statistical approach for real-time robust background subtraction and shadow detection. In:Proc of IEEE Int Conf Computer Vision. Kcrkyra,1999,1-19
[37]Benedek C, Sziranyi T. Bayesian foreground and shadow detection in uncertain frame rate surveillance videos. IEEE Transactions on Image Processing,2008, 17(4):608-621
[38]Jung C R. Efficient background subtraction and shadow removal for monochromatic video sequences. IEEE Transactions on Multimedia,2009, 11(3):571-577
[39]Cucchiara R, Grana C, Piccardi M. Improving Shadow Suppression in Moving Object Detection with HSV Color Information. In:Proc of IEEE Int'l Conf Intelligent Transportation Systems. Oakland,2001,334-339
[40]Salvador E, Cavallaro A, Ebrahimi T. Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding,2004,95(2):238-259
[41]Xiao M, Han C Z, Zhang L. Moving shadow detection and removal for traffic sequences. International Journal of Automation and Computing,2007,4(1):38-46
[42]Yoneyama Yeh C H, Kuo C C J. Moving Cast Shadow Elimination for Robust Vehicle Extraction based on 2D Joint Vehicle/Shadow Models. In:Proc of The IEEE Conference on advanced video and signal based surveillance. Miami,2003,229-236
[43]Mikic I, Cosman P C, Kogut G T, et al. Moving shadow and object detection in traffic scenes. In:Proc of 15th International Conference on Pattern recognition. Barcelona,2000,321-324
[44]Cucchinara R, Grana C, Neri G, et al. The Sakbot system for moving object detection and tracking. Video-Based Surveillance Systems—Computer Vision and Distributed Processing,2001:145-157
[45]Kumar P, Sengupta K, Lee A, et al. A Comparative Study of Different Color Spaces for Foreground and Shadow Detection for Traffic Monitoring System. In: Proc of IEEE Conference on Intelligent Transportation Systems. Singapore, 2002,100-105
[46]阮秋琦.数字图像处理学(第二版).北京：电子工业出版社.2007,98-99
[47]Smith J R, Chang S F. Tools and Techniques for Color Image Retrievel. Storage & Retrieval for Image and Video Databases Ⅳ,1996,2670:426-437
[48]Chien S Y, Ma S Y, Chen L G. Efficient Moving Object Segmentation Algorithm Using Background Registration Technique. IEEE Transactions on Circuits and Systems for Video Technology,2002,12(7):577-586
[49]Xu D, Liu J Z, Li X L, et al. Insignificant Shadow Detection for Video Segmentation. IEEE Transactions on Circuits and Systems for Video Techology, 2005,15(8):1058-1064
[50]Leone L, Distante C. Shadow detection for moving objects based on texture analysis. Pattern Recognition,2007,40(4):1222-1233
[51]姚敏.数字图像处理.北京： 机械工业出版社.2006,226-229
[2]中国研究报告网.2009-2012年视频监控系统行业发展前景分析及投资风险预测报告.www.a8m.cn,2009-10-28
[3]杜友田,陈峰,徐文立等.基于视觉的人的运动识别综述.电子学报,2007,35(1)：84-90
[4]Collins R T, Lipton A J, Kanade T, et al. A System for Video Surveillance and Monitoring. In:Proc of American Nuclear Society (ANS) Eighth International Topical Meeting on Robotics and Remote Systems. Carnegie Mellon,2000,25-29
[5]HID. Human Identification at a Distance. cc.gatech.edu/cpl/projects/hid/,2000
[6]VACE. Video Analysis and Content Extraction for Defense Intelligence. www.informedia.cs.cmu.edu,2003
[7]Haritaoglu I, Harwood D, Davis L S. W4:real-time surveillance of people and their activities. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2000,22(8):809-830
[8]Remagnino P, Tan T, Baker K. Multi-agent visual sruveillance of dynamic scenes. Image and Vision Computing,1998,16(8):529-532
[9]Naylor M, Attwood C I. Final Report IST 1999-11287:ADVISOR. European:The ADVISOR Consortium,2003
[10].Matsuyama T. Cooperative Distributed Vision:Dynamic Integration of Visual Perception, Action, and Communication. In:Proc of The 23rd Annual German Conference on Artificial Intelligence:Advances in Artificial Intelligence. Bonn, 1999,75-88
[11]Renno J P, Makris D, Jones G A. Object Classification in Visual Surveillance Using Adaboost. In:Proc of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis,2007,1-8
[12]Zhou H, Yuan Y, Shi C M. Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding,2009,113(3):345-352
[13]Ullman S. Object recognition and segmentation by a fragment-based hierarchy. Trends in Cognitive Sciences,2007, 11(2):58-64
[14]代科学,李国辉,涂丹等.监控视频运动目标检测减背景技术的研究现状和展望.中国图象图形学报,2006,11(7)：919-927
[15]Horn B K P, Schunck B G. Determining optical flow. Artificial Intelligence, 1981,17(1-3):185-203
[16]施家栋,王建中,王红茹.基于光流的人体运动实时检测方法.北京理工大学学报,2008,28(9)：794-797
[17]郑锦,李波.视频序列中运动对象检测技术的研究现状与展望.计算机应用研究,2008,25(12)：3534-3540
[18]Neri A, Colonnese S, Russo G, et al. Automatic moving object and background separation. Signal Processing,1998,66(2):219-232
[19]Lipton A J, Fujiyoshi H, Patil R S. Moving target classification and tracking from real-time video. In:Proc of The Fourth IEEE Workshop on Applications of Computer Vision. New Jersey,1998,8-14.
[20]Piccardi M. Background subtraction techniques:a review. In:Proc of IEEE Conference on Systems, Man and Cybernetics. Hague,2004,3099-3104
[21]Hu J S, Su T M. Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance. EURASIP Journal on Advances in Signal Processing,2007,2007:1-14
[22]Chen Y T, Chen C S, Huang C R, et al. Efficient hierarchical method for background subtraction. Pattern Recognition,2007,40(10):2706-2715
[23]Hou Z Q, Han C Z. A background reconstruction algorithm based on pixel intensity classification in remote video surveillance System. In:Proc of the 7th International Conference on Information Fusion. Stockholm,2004,754-759
[24]Cucchiara R, Grana C, Piccardi M. Detecting Moving Objects, Ghosts and Shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1337-1342
[25]Toyama K, Krumm J, Brumitt B, et al. Wallfower:Principles and Practice of Background Maintenance. In:Proc of the 7th IEEE International Conference on Computer Vision. Kerkyra,1999,255-261
[26]Karmann K P, Brandt A. Moving Object Recognition Using an Adaptive Background Memory. In:Proc of Time Varying Image Processing and Moving Object Recognition. Cappellini,1990,289-296
[27]Ridder C, Munkelt O, Kirchner H. Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. In:Proc of the International Conference on Recent Advances in Mechatronics. Instanbul,1995,193:199
[28]Oliver N, Rosario B, Pentland A. A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):831-843
[29]Stauffer C, Grimson W E L. Adaptive background mixture models for real—time tracking. In:Proc of IEEE Conference on Computer Vision and Pattern Recognition. Colorado,1999,246-252
[30]Stauffer C, Grimson W E L. Learning Patterns of Activity Using Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, 22(8):747-757
[31]Heikkila J, Silve 0. A Real-Time System for Monitoring of Cyclists and Pedestrians. Image and Vision Computing,2004,22(7):563-570
[32]Salvador E, Cavallaro A, Ebrahimi T. Shadow identification and classification using invariant color models. Computer vision and image understanding,2004, 95(2):238-259
[33]Prati A, Mikic I, Trivedi M M. Detecting Moving Shadows:Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(7):918-923
[34]Wern C R, Azarbaycjani A, Darrell T, et al. Pfinder:Real time tracking of human body. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997, 19(7):780-785
[35]KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. In:Proc of the Second European Workshop on Advanced Video Based Surveillance Systems. Kingston,2001, 149-158
[36]Horprasert T, Harwood D, Davis L S. A statistical approach for real-time robust background subtraction and shadow detection. In:Proc of IEEE Int Conf Computer Vision. Kcrkyra,1999,1-19
[37]Benedek C, Sziranyi T. Bayesian foreground and shadow detection in uncertain frame rate surveillance videos. IEEE Transactions on Image Processing,2008, 17(4):608-621
[38]Jung C R. Efficient background subtraction and shadow removal for monochromatic video sequences. IEEE Transactions on Multimedia,2009, 11(3):571-577
[39]Cucchiara R, Grana C, Piccardi M. Improving Shadow Suppression in Moving Object Detection with HSV Color Information. In:Proc of IEEE Int'l Conf Intelligent Transportation Systems. Oakland,2001,334-339
[40]Salvador E, Cavallaro A, Ebrahimi T. Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding,2004,95(2):238-259
[41]Xiao M, Han C Z, Zhang L. Moving shadow detection and removal for traffic sequences. International Journal of Automation and Computing,2007,4(1):38-46
[42]Yoneyama Yeh C H, Kuo C C J. Moving Cast Shadow Elimination for Robust Vehicle Extraction based on 2D Joint Vehicle/Shadow Models. In:Proc of The IEEE Conference on advanced video and signal based surveillance. Miami,2003,229-236
[43]Mikic I, Cosman P C, Kogut G T, et al. Moving shadow and object detection in traffic scenes. In:Proc of 15th International Conference on Pattern recognition. Barcelona,2000,321-324
[44]Cucchinara R, Grana C, Neri G, et al. The Sakbot system for moving object detection and tracking. Video-Based Surveillance Systems—Computer Vision and Distributed Processing,2001:145-157
[45]Kumar P, Sengupta K, Lee A, et al. A Comparative Study of Different Color Spaces for Foreground and Shadow Detection for Traffic Monitoring System. In: Proc of IEEE Conference on Intelligent Transportation Systems. Singapore, 2002,100-105
[46]阮秋琦.数字图像处理学(第二版).北京：电子工业出版社.2007,98-99
[47]Smith J R, Chang S F. Tools and Techniques for Color Image Retrievel. Storage & Retrieval for Image and Video Databases Ⅳ,1996,2670:426-437
[48]Chien S Y, Ma S Y, Chen L G. Efficient Moving Object Segmentation Algorithm Using Background Registration Technique. IEEE Transactions on Circuits and Systems for Video Technology,2002,12(7):577-586
[49]Xu D, Liu J Z, Li X L, et al. Insignificant Shadow Detection for Video Segmentation. IEEE Transactions on Circuits and Systems for Video Techology, 2005,15(8):1058-1064
[50]Leone L, Distante C. Shadow detection for moving objects based on texture analysis. Pattern Recognition,2007,40(4):1222-1233
[51]姚敏.数字图像处理.北京： 机械工业出版社.2006,226-229