智能交通系统车辆检测算法研究
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摘要
车辆交通是现代社会的重要标志之一,但随着经济的发展,它在给人们的生活提供便利的同时也产生了一系列社会和环境问题。智能交通系统(IntelligentTransportationSystem,简称ITS)利用通信技术、控制技术、传感器技术、运筹学、人工智能和计算机技术的有效集成,其目的在于充分利用现有道路基础设施资源,改善车、路、人之间的相互作用,提高系统的安全性、高效性与舒适性,从而从整体上提高交通的经济性,正在成为世界各国解决交通拥塞、交通事故频发、土地和能源短缺、交通环境污染以及由此导致的经济损失等的热点研究问题,具有较高的社会效益和巨大的市场应用前景。
目前,智能交通系统在理论和应用上都面临着很多难题,国内外大批学者投身于该领域的研究和探索,并且取得了大量的成果,而运动车辆的实时检测作为智能交通系统的核心部分之一,更是成为了研究热点。本文是在这些成果的基础上,对智能交通系统中车辆的精确检测及分割技术进行了研究。主要工作如下:
首先,对混合高斯背景建模进行了比较系统的分析,并针对混合高斯背景模型现实应用中的问题,提出一种快速的混合高斯背景建模改进算法,该算法利用场景内光照条件的变化提出了一个自适应背景更新率,根据各像素点颜色值出现的混乱程度不同采取不同的高斯函数参数更新机制,通过对交通监控图像序列进行实验,该算法能在保证背景建模与运动车辆检测效果的同时,使混合高斯模型的背景更新速度及算法处理速度有了较大提高。
其次,针对图像后处理去噪,本文介绍了几种常用的滤波方法,着重讨论了形态学中的膨胀、腐蚀运算在去噪中的应用,并取得了很好的效果。
最后,提出了一种利用色度畸变和纹理特征进行车辆阴影抑制的方法。该方法分析了场景点在存在阴影前后色度的分布规律以及纹理的互相关性,根据亮度信息和饱和度信息选择不同的阴影消除机制,利用颜色向量夹角和纹理特征进行阴影的消除。
The vehicle transportation is one of the important symbols of modern society, but it brings a series of social and environmental problems as well as advantages in our lives. Intelligent Transportation System(ITS) is a system for integrating with communication technology, control technology, sensor technology, operational research, artificial intelligent and computer technology. The aim of ITS is to make full use of the available road facilities, enhancing the security, high efficiency and comfortable of transportation system, and then improve the economical efficiency of the whole system. ITS is one popular research problem of all countries to solve the traffic jam, the traffic accident, the lack of soil and energy resource, the pollution of the environment, and the economic lose by all of them. The application of ITS will bring both economic and social benefit.
As to computer Intelligent Transportation System, there are still many problems no matter in theory research or in applications. Large numbers of researchers devoted themselves in the area and have already achieved many progresses. Moving vehicle detection is the most important one of the fundamental tasks for Intelligent Transportation System, which is active topic widely researched in the last decade around the world. Based on the achievements, the dissertation studied the technologies of accurate detection and segmentation of moving vehicles in Intelligent Transportation System. The main work can be summarized as follows:
Firstly, we research Gaussian Mixture Model through systematical analysis, and an improved mixture Gaussian background modeling algorithm is presented to reduce the computational cost of traditional mixture Gaussian algorithms. The improved mixture Gaussian-based background model updates the parameters of Gaussian according to the frequency of a pixel value changes. Experimental results show that our algorithm can improve the processing speed greatly and detect moving vehicle accurately.
Secondly, as to noise removal of image post-processing, several commonly used methods of removing noise are introduced in the dissertation. Dilation and Erosion in morphology are discussed emphatically in the application of de-noising in the experiments of this dissertation and results are achieved effectively.
Thirdly, a shadow suppression method for vehicle detection which combines chrominance distortion and texture feature is proposed. The distribution rule of the chrominance and the cross correlation of texture after and before shadow appears are analyzed, different shadow suppression mechanism is selected according to brightness and saturation information, and chrominance distortion and texture feature are used to eliminate shadow.
目前,智能交通系统在理论和应用上都面临着很多难题,国内外大批学者投身于该领域的研究和探索,并且取得了大量的成果,而运动车辆的实时检测作为智能交通系统的核心部分之一,更是成为了研究热点。本文是在这些成果的基础上,对智能交通系统中车辆的精确检测及分割技术进行了研究。主要工作如下:
首先,对混合高斯背景建模进行了比较系统的分析,并针对混合高斯背景模型现实应用中的问题,提出一种快速的混合高斯背景建模改进算法,该算法利用场景内光照条件的变化提出了一个自适应背景更新率,根据各像素点颜色值出现的混乱程度不同采取不同的高斯函数参数更新机制,通过对交通监控图像序列进行实验,该算法能在保证背景建模与运动车辆检测效果的同时,使混合高斯模型的背景更新速度及算法处理速度有了较大提高。
其次,针对图像后处理去噪,本文介绍了几种常用的滤波方法,着重讨论了形态学中的膨胀、腐蚀运算在去噪中的应用,并取得了很好的效果。
最后,提出了一种利用色度畸变和纹理特征进行车辆阴影抑制的方法。该方法分析了场景点在存在阴影前后色度的分布规律以及纹理的互相关性,根据亮度信息和饱和度信息选择不同的阴影消除机制,利用颜色向量夹角和纹理特征进行阴影的消除。
The vehicle transportation is one of the important symbols of modern society, but it brings a series of social and environmental problems as well as advantages in our lives. Intelligent Transportation System(ITS) is a system for integrating with communication technology, control technology, sensor technology, operational research, artificial intelligent and computer technology. The aim of ITS is to make full use of the available road facilities, enhancing the security, high efficiency and comfortable of transportation system, and then improve the economical efficiency of the whole system. ITS is one popular research problem of all countries to solve the traffic jam, the traffic accident, the lack of soil and energy resource, the pollution of the environment, and the economic lose by all of them. The application of ITS will bring both economic and social benefit.
As to computer Intelligent Transportation System, there are still many problems no matter in theory research or in applications. Large numbers of researchers devoted themselves in the area and have already achieved many progresses. Moving vehicle detection is the most important one of the fundamental tasks for Intelligent Transportation System, which is active topic widely researched in the last decade around the world. Based on the achievements, the dissertation studied the technologies of accurate detection and segmentation of moving vehicles in Intelligent Transportation System. The main work can be summarized as follows:
Firstly, we research Gaussian Mixture Model through systematical analysis, and an improved mixture Gaussian background modeling algorithm is presented to reduce the computational cost of traditional mixture Gaussian algorithms. The improved mixture Gaussian-based background model updates the parameters of Gaussian according to the frequency of a pixel value changes. Experimental results show that our algorithm can improve the processing speed greatly and detect moving vehicle accurately.
Secondly, as to noise removal of image post-processing, several commonly used methods of removing noise are introduced in the dissertation. Dilation and Erosion in morphology are discussed emphatically in the application of de-noising in the experiments of this dissertation and results are achieved effectively.
Thirdly, a shadow suppression method for vehicle detection which combines chrominance distortion and texture feature is proposed. The distribution rule of the chrominance and the cross correlation of texture after and before shadow appears are analyzed, different shadow suppression mechanism is selected according to brightness and saturation information, and chrominance distortion and texture feature are used to eliminate shadow.
引文
1陆化普.城市现代化管理.北京:人民交通出版社,1999.
2周志宇,汪亚明,黄文清.计算抓视觉在交通监控中的应用,,实用案例,2003,(1):51-53.
3张泽旭,李金宗,李宁宁.基于光流场分割和Canny边缘提取融合算法的运动目标检测.电子学报, 2003, 31(9): 1299-1302.
4屈有山,田维坚,李英才.基于并行隔帧差分光流场与灰度分析综合算法的运动目标检测.光子学报, 2003, 32(2): 182-186.
5 Sasa G, Loncaric S. Spatio-temporal image segmentation using optical flow and clustering algorithm. First Int’l Workshop on Image and Signal Processing and Analysis. Pula, Croatia,2000: 63-68.
6 Smith S M, Brady J M. ASSET-2: Real-time motion segmentation and shape tracking. IEEE Trans, 1995, 8(17): 814-820.
7 Thompson W B, Pong T C. Detecting moving object. Int J Comp Vision. 1990, 4: 39-57.
8 Jong Bae Kim, Hang Joon Kim. Efficient region-based motion segmentation for a video monitoring system. Pattern Recognition Letters 2003, 24(1-3): 113-128.
9 Needham C J, Boyle R D. Tracking multiple sports players through occlusion, congestion and scale. British Machine Vision Conference, 2001, 1: 93-102.
10 Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. European Conference on Computer Vision. Dublin, Ireland, 2000. 751-767.
11 Xiaobo Li, Zhi-Qiang Liu, Ka-Ming Leung. Detection of vehicles from traffic scenes using fuzzy integrals. Pattern Recognition 2002, 35(4): 967-980.
12 Vieren C, Cabestaing F, Postaire J. Catching moving objects with snakes for motion tracking. Pattern Recognition Letters, 1995, 16(7): 679-685.
13 I.Mikic, P.Cosman, G.Kogut, and M.M.Trivedi. Moving shadow and object detection in traffic scenes, Proceedings of Int’t Conference on Pattern Recognition. 2000, 1: 321-324.
14 R.Cucchiara, C.Grana, M.Piccardi, and A.Prati. Detecting objects, shadows and ghosts in video streams by exploiting color and motion information.Proceedings of the IEEE Int’l Conference on Image Analysis and Processing, 2001: 360-365.
15 Rita Cucchiara, et al. Improving shadow suppression in moving object detection with HSV color information. Proc.of the 4th International IEEE Conference on Intelligent Transportation System, 2001. 334-339.
16 Rosin P L, Ellis T. Image difference threshold strategies and shadow detection. Proc.of the 6th British Machine Vision Conference, 1994, 347-356.
17 Akio Yoneyama, Chia H. Yeh and C.–C. Jay Kuo. Moving cast shadow elimination for robot vehicle extraction based on 2D joint vehicle/shadow models. Proceeding of the IEEE Conference on Advanced Video and Signal Based Surveillance. 2003. 229-236.
18 Alessandro Bevilacqua. Effective shadow detection in traffic monitoring applications. Journal of WSCG. 2003, 11(1): 57-64.
19 Jun-Wei Hsieh, Wen-Fong Hu, Chia-Jung Chang, Yung-Sheng Chen. Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image and Visio Computing. 2003, 21(6): 505-516.
20 W.Hu, T.Tan, L.Wang, S.Maybank. A Survey on Visual Survellance of Object Motion and Behaviors. IEEE Trans. SMC, 2004, 34(3): 334-352.
21李玉山,数字视觉视频技术.西安电子科技人学出版社,2006.
22 Refael C.Gonzalez, Richard E.Woods著,阮秋琦,阮宇智等译,数字图象处理第二版,北京电子业出版社,2003.
23 Takaba S. A Traffic low Measuring System Using a Solid State Sensor. Poc. IEEE Conf. on Road Traffic Data Collection, London, UK, 1984:45-66.
24 Abutaleb A S. Automatic Thresholding of Gray-level Pictures Using Two-dimensional Entropy. Computer Vision Graphics and Image Processing, 1989, 47(2):22-32.
25将刚毅,郁梅,叶锡恩.一种基于视觉的车辆跟踪及交通流量参数估计新方法.电路与系统学报, 2001, 6(4):69-73.
26陈震.图像序列光流场计算及三维场景恢复研究.西北工业大学, 2003.
27 B.K.P.Horn, B.G.Schunck. Determining Optical Flow. Artificial Intelligence, 1981, 17[1-3]:185-203.
28 G.Adiv. Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Transactions on Pattern Analysisand Machine Intelligence, 1985, 7:384-401.
29 J.L.Barron, D.J.Fleet, S.S.Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 1994, 12[1]:43-77.
30 Stauffer C, Grimson W.E.L. Adaptive Background Mixture Models for real-time Tracking. In Proceedings, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2:246-252.
31 Daniela Hall, J.Nascimento, P.Ribeiro, E.Andrade. Comparison of Target Detection Algorithms Using Adaptive Background Models. International workshop on Performance evaluation of Tracking and Surveillance–oct 2005.
32杨淑莹. VC++图像处理程序设计(第2版).北京:清华大学出版社北京交通大学出版社, 2005, 86-103, 189-207.
33何斌,马天予,王运坚,朱红莲. Visual C++数字图像处理(第二版).北京:人民邮电出版社, 2002.
34张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波.计算机辅助设计与图形学学报, 2005, 17(2):295-299.
35 P.Kaew Tra Kul Pong, R.Bowden. An Improved Adaptive Background Mixture Model for real-time Tracking with Shadow Detection. In Proc. 2nd European Workshop Advanced Video Based Surveillance System, Spet 2001.
36 C.Jiang, M.O.Ward. Shadow segmentation and classification in a constrained environment. CVGIP: Image Understanding, 1994, 59,(2):213-225.
37 Yang Wang, Kia-Fock Loe, Jian-Kang Wu. A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.2, Feb 2006.
38 Graham D.Finlayson, Steven D.Hordley, Cheng Lu, Mark S.Drew. On the Removal of Shadows from Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.1, Jan 2006.
39 O.Schreer et al. Fast and robust shadow detection in videoconference Applications, 4th IEEE Intern. Symposium on Video Process. And Multimedia Comm., 2002 , 371-375.
40 G.D. Finlayson, S.D. Hordley, M.S. Drew. Removing Shadows from Images. in Proc of European Conf on Computer Vision, 2002, Vol.4, 823-836.
41 Chen Baisheng, Lei Yunqi. Indoor and Outdoor People Detection and Shadow Suppression by Exploiting HSV Color Information. Proceedings of the 4thInternational Conference on Computer and Information Technology[C], 2004, 137-142.
42 Andrea Prati, Ivana Mikic, Mohan M.Trivedi, Rita Cucchiara. Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence [J], Jul.2003, 25(7): 918-923.
43 Yuqi Wang, Miao Tang, Guangxi Zhu. An Improved Cast Shadow Detection Method with Edge Refinement, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), Vol.02: 794-799.
44王典,程咏梅等.基于混合高斯模型的阴影抑制算法.计算机应用,第26卷第五期, 2006年5月, 1021-1023.
45 A.Prati, I.Mikic, C.Grana, M.M.Trivedi. Shadow Detection Algorithms for Traffic Flow Analysis: a Comparative Study. in Proceedings of the IEEE Int. Conference on Intelligent Transportation Systems,2001.
46 E. Salvador, A. Cavallaro, T. Ebrahimi. Shadow Identification and Classification Using Invariant Color Models. in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2001, 1545-1548.
47 T. Gevers, A.W.M. Smeulders. Color-based object recognition, Pattern Recogn. 32 (1999), 453-464.
48 E.Salvador, A.Cavallaro, T.Ebrahimi. Cast Shadow Segmentation Using Invariant Color Features. Computer Vision and Image Understanding[J], Volume 95, Issue 2 (Aug 2004), 238-259.
49 Sohail Nadimi, Bir Bhanu. Physical Models for Moving Shadow and Object Detection in Video. IEEE Transactions on Pattern Analysis and Machine Intelligence [J], Aug2004, 26(8): 1079-1087.
50 S.A. Shafer. Using Color to Separate Reflection Components. Color Research and Application, 1985, 10(4): 210-218.
51 S.K.Nayar, R.M.Bolle. Reflectance Based Object Recognition. Int J. Computer Vision, 1996, 17(3): 219-240.
52刘龙,刘贵忠,刘洁瑜,王占辉.一种基于MPEG压缩域的运动对象分割算法.西安交通大学学报.2004,38(2): 1264-1267.
53 P. Kumar, K. Sengupta, A. Lee. A Comparative Study of Different Color Spaces for Foreground and Shadow Detection for Traffic Monitoring System. The IEEE5th International Conference on Intelligent Transportation Systems, 2002: 100-105.
54 J.Stauder, R.Mech, J.Ostermann. Detection of Moving Cast Shadows for Object Segmentation, IEEE Trans. Multimedia, 1999, 1(1): 65-76.
55 A.Branca, G.Attolico, A.Distante. Cast shadow removing in foreground segmentation. The 16th International Conference on Pattern Recognition , Aug. 2002, vol. 1:214-217.
56 D.Grest, J.M.Frahm, R.Koch. A Color Similarity Measure for Robust Shadow Removal in Real-Time.VMV. Munich, Germany, Nov.2003.
2周志宇,汪亚明,黄文清.计算抓视觉在交通监控中的应用,,实用案例,2003,(1):51-53.
3张泽旭,李金宗,李宁宁.基于光流场分割和Canny边缘提取融合算法的运动目标检测.电子学报, 2003, 31(9): 1299-1302.
4屈有山,田维坚,李英才.基于并行隔帧差分光流场与灰度分析综合算法的运动目标检测.光子学报, 2003, 32(2): 182-186.
5 Sasa G, Loncaric S. Spatio-temporal image segmentation using optical flow and clustering algorithm. First Int’l Workshop on Image and Signal Processing and Analysis. Pula, Croatia,2000: 63-68.
6 Smith S M, Brady J M. ASSET-2: Real-time motion segmentation and shape tracking. IEEE Trans, 1995, 8(17): 814-820.
7 Thompson W B, Pong T C. Detecting moving object. Int J Comp Vision. 1990, 4: 39-57.
8 Jong Bae Kim, Hang Joon Kim. Efficient region-based motion segmentation for a video monitoring system. Pattern Recognition Letters 2003, 24(1-3): 113-128.
9 Needham C J, Boyle R D. Tracking multiple sports players through occlusion, congestion and scale. British Machine Vision Conference, 2001, 1: 93-102.
10 Elgammal A, Harwood D, Davis L. Non-parametric model for background subtraction. European Conference on Computer Vision. Dublin, Ireland, 2000. 751-767.
11 Xiaobo Li, Zhi-Qiang Liu, Ka-Ming Leung. Detection of vehicles from traffic scenes using fuzzy integrals. Pattern Recognition 2002, 35(4): 967-980.
12 Vieren C, Cabestaing F, Postaire J. Catching moving objects with snakes for motion tracking. Pattern Recognition Letters, 1995, 16(7): 679-685.
13 I.Mikic, P.Cosman, G.Kogut, and M.M.Trivedi. Moving shadow and object detection in traffic scenes, Proceedings of Int’t Conference on Pattern Recognition. 2000, 1: 321-324.
14 R.Cucchiara, C.Grana, M.Piccardi, and A.Prati. Detecting objects, shadows and ghosts in video streams by exploiting color and motion information.Proceedings of the IEEE Int’l Conference on Image Analysis and Processing, 2001: 360-365.
15 Rita Cucchiara, et al. Improving shadow suppression in moving object detection with HSV color information. Proc.of the 4th International IEEE Conference on Intelligent Transportation System, 2001. 334-339.
16 Rosin P L, Ellis T. Image difference threshold strategies and shadow detection. Proc.of the 6th British Machine Vision Conference, 1994, 347-356.
17 Akio Yoneyama, Chia H. Yeh and C.–C. Jay Kuo. Moving cast shadow elimination for robot vehicle extraction based on 2D joint vehicle/shadow models. Proceeding of the IEEE Conference on Advanced Video and Signal Based Surveillance. 2003. 229-236.
18 Alessandro Bevilacqua. Effective shadow detection in traffic monitoring applications. Journal of WSCG. 2003, 11(1): 57-64.
19 Jun-Wei Hsieh, Wen-Fong Hu, Chia-Jung Chang, Yung-Sheng Chen. Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image and Visio Computing. 2003, 21(6): 505-516.
20 W.Hu, T.Tan, L.Wang, S.Maybank. A Survey on Visual Survellance of Object Motion and Behaviors. IEEE Trans. SMC, 2004, 34(3): 334-352.
21李玉山,数字视觉视频技术.西安电子科技人学出版社,2006.
22 Refael C.Gonzalez, Richard E.Woods著,阮秋琦,阮宇智等译,数字图象处理第二版,北京电子业出版社,2003.
23 Takaba S. A Traffic low Measuring System Using a Solid State Sensor. Poc. IEEE Conf. on Road Traffic Data Collection, London, UK, 1984:45-66.
24 Abutaleb A S. Automatic Thresholding of Gray-level Pictures Using Two-dimensional Entropy. Computer Vision Graphics and Image Processing, 1989, 47(2):22-32.
25将刚毅,郁梅,叶锡恩.一种基于视觉的车辆跟踪及交通流量参数估计新方法.电路与系统学报, 2001, 6(4):69-73.
26陈震.图像序列光流场计算及三维场景恢复研究.西北工业大学, 2003.
27 B.K.P.Horn, B.G.Schunck. Determining Optical Flow. Artificial Intelligence, 1981, 17[1-3]:185-203.
28 G.Adiv. Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Transactions on Pattern Analysisand Machine Intelligence, 1985, 7:384-401.
29 J.L.Barron, D.J.Fleet, S.S.Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 1994, 12[1]:43-77.
30 Stauffer C, Grimson W.E.L. Adaptive Background Mixture Models for real-time Tracking. In Proceedings, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2:246-252.
31 Daniela Hall, J.Nascimento, P.Ribeiro, E.Andrade. Comparison of Target Detection Algorithms Using Adaptive Background Models. International workshop on Performance evaluation of Tracking and Surveillance–oct 2005.
32杨淑莹. VC++图像处理程序设计(第2版).北京:清华大学出版社北京交通大学出版社, 2005, 86-103, 189-207.
33何斌,马天予,王运坚,朱红莲. Visual C++数字图像处理(第二版).北京:人民邮电出版社, 2002.
34张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波.计算机辅助设计与图形学学报, 2005, 17(2):295-299.
35 P.Kaew Tra Kul Pong, R.Bowden. An Improved Adaptive Background Mixture Model for real-time Tracking with Shadow Detection. In Proc. 2nd European Workshop Advanced Video Based Surveillance System, Spet 2001.
36 C.Jiang, M.O.Ward. Shadow segmentation and classification in a constrained environment. CVGIP: Image Understanding, 1994, 59,(2):213-225.
37 Yang Wang, Kia-Fock Loe, Jian-Kang Wu. A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.2, Feb 2006.
38 Graham D.Finlayson, Steven D.Hordley, Cheng Lu, Mark S.Drew. On the Removal of Shadows from Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.1, Jan 2006.
39 O.Schreer et al. Fast and robust shadow detection in videoconference Applications, 4th IEEE Intern. Symposium on Video Process. And Multimedia Comm., 2002 , 371-375.
40 G.D. Finlayson, S.D. Hordley, M.S. Drew. Removing Shadows from Images. in Proc of European Conf on Computer Vision, 2002, Vol.4, 823-836.
41 Chen Baisheng, Lei Yunqi. Indoor and Outdoor People Detection and Shadow Suppression by Exploiting HSV Color Information. Proceedings of the 4thInternational Conference on Computer and Information Technology[C], 2004, 137-142.
42 Andrea Prati, Ivana Mikic, Mohan M.Trivedi, Rita Cucchiara. Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence [J], Jul.2003, 25(7): 918-923.
43 Yuqi Wang, Miao Tang, Guangxi Zhu. An Improved Cast Shadow Detection Method with Edge Refinement, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), Vol.02: 794-799.
44王典,程咏梅等.基于混合高斯模型的阴影抑制算法.计算机应用,第26卷第五期, 2006年5月, 1021-1023.
45 A.Prati, I.Mikic, C.Grana, M.M.Trivedi. Shadow Detection Algorithms for Traffic Flow Analysis: a Comparative Study. in Proceedings of the IEEE Int. Conference on Intelligent Transportation Systems,2001.
46 E. Salvador, A. Cavallaro, T. Ebrahimi. Shadow Identification and Classification Using Invariant Color Models. in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2001, 1545-1548.
47 T. Gevers, A.W.M. Smeulders. Color-based object recognition, Pattern Recogn. 32 (1999), 453-464.
48 E.Salvador, A.Cavallaro, T.Ebrahimi. Cast Shadow Segmentation Using Invariant Color Features. Computer Vision and Image Understanding[J], Volume 95, Issue 2 (Aug 2004), 238-259.
49 Sohail Nadimi, Bir Bhanu. Physical Models for Moving Shadow and Object Detection in Video. IEEE Transactions on Pattern Analysis and Machine Intelligence [J], Aug2004, 26(8): 1079-1087.
50 S.A. Shafer. Using Color to Separate Reflection Components. Color Research and Application, 1985, 10(4): 210-218.
51 S.K.Nayar, R.M.Bolle. Reflectance Based Object Recognition. Int J. Computer Vision, 1996, 17(3): 219-240.
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