基于第二代Bandelet变换的人体检测方法研究
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摘要
人体检测是计算机视觉中一个重要的研究领域,在视频监控、智能汽车及智能交通、机器人和高级人机交互等领域具有广泛而重要的应用。然而,由于人体自身姿态的变化、衣服的多样性和光照等因素的影响,人体的外观变化非常大,导致人体检测成为一个非常困难的问题。为了提高人体检测的正确率,本论文由图像的几何流特性出发,提出了基于第二代Bandelet变换的人体检测特征提取新方法,并将其应用于静态及动态人体图像的检测。
在第二代Bandelet变换的基础上,本论文就降低特征提取的时间复杂度,提高运动区域的分割准确度以及提高人体检测的鲁棒性等方面做了大量工作,主要涉及基于统计学习的人体检测方法、光流法、图像分割方法、基于部位的人体检测方法等。
本文主要工作如下:
1)提出了一种基于第二代Bandelet变换的图像特征提取及人体检测方法。即利用优化后的Bandelet变换中的Bandelet系数及其统计特征作为图像的特征,分类并检测图像中的人体。首先通过实验确立了Bandelet变换用于人体图像特征提取时的最优参数和附加的统计特征,然后利用线性SVM分类器进行分类,并进行了分类性能与人体检测测试。
2)提出了一种基于光流与几何流,用于运动人体视频的人体检测方法。即通过计算光流场来进行区域分割,通过基于几何流的Bandelet变换来进行特征提取和分类。其间对光流场进行了去噪,以利于提取正确的运动人体区域。
3)使用了基于部位的人体检测方法,提出了一种分块提取特征的部位检测方法。即先提取整幅图像的Bandelet特征,然后将特征矢量细分成若干小块;按整个人体的比例提取运动区域输入全人体分类器进行分类,得到候选人体,然后依次检测各部分,同时依据各个部位检测器的检测似然度进行判定。
Human detection has attracted a lot of research interests in recent years, because it has several important applications in computer vision, e.g., video surveillance, smart vehicles, human-robot interaction and content-based image filtration. However, human has been proven to be a much more difficult object to detect, because of the wide variability in appearance, due to clothing, articulation and illumination conditions that are not common in outdoor scenes. In this paper, based on the second generation Bandelet transform, we followed the geometric flow of images and presented a new feature extraction method to improve the accuracy of human detection in images.
Besides, many researches had been carried, focused on reducing time cost, improving the accuracy of region segmentation and enhancing the robust of detection. It involved the method of optical flow, image segmentation and the human detection method based on body-parts. Specifically, the paper proposed three original human detection methods based on second generation Bandelet transform.
1) Based on the second generation Bandelet transform, we followed the geometric flow of images and presented a new feature extraction method to improve the accuracy of human detection in images. Here the Bandelet coefficients and their statistical values were extracted as the features of human images, combined with linear SVM classifier, then we can classify and detect human in images.
2) Different from most of the previous work, we detect motion human by region segmentation and classification through machine learning. In our method, based on optical flow, region segmentation is carried firstly and then, based on geometric flow, Bandelet transform is used to do feature extraction and classification. Some treatments were carried after optical flow field computation to de-noising and some improvements in Bandelet transform were used to reduce time cost of feature extraction.
3) The third method focused on enhancing the robust of human detection by using body-parts and feature of Bandelet transform. Experiments showed that this algorithm could be an effective way to detection human in static or motion images, and it is worthy that we should do more effort into this in the future research.
在第二代Bandelet变换的基础上,本论文就降低特征提取的时间复杂度,提高运动区域的分割准确度以及提高人体检测的鲁棒性等方面做了大量工作,主要涉及基于统计学习的人体检测方法、光流法、图像分割方法、基于部位的人体检测方法等。
本文主要工作如下:
1)提出了一种基于第二代Bandelet变换的图像特征提取及人体检测方法。即利用优化后的Bandelet变换中的Bandelet系数及其统计特征作为图像的特征,分类并检测图像中的人体。首先通过实验确立了Bandelet变换用于人体图像特征提取时的最优参数和附加的统计特征,然后利用线性SVM分类器进行分类,并进行了分类性能与人体检测测试。
2)提出了一种基于光流与几何流,用于运动人体视频的人体检测方法。即通过计算光流场来进行区域分割,通过基于几何流的Bandelet变换来进行特征提取和分类。其间对光流场进行了去噪,以利于提取正确的运动人体区域。
3)使用了基于部位的人体检测方法,提出了一种分块提取特征的部位检测方法。即先提取整幅图像的Bandelet特征,然后将特征矢量细分成若干小块;按整个人体的比例提取运动区域输入全人体分类器进行分类,得到候选人体,然后依次检测各部分,同时依据各个部位检测器的检测似然度进行判定。
Human detection has attracted a lot of research interests in recent years, because it has several important applications in computer vision, e.g., video surveillance, smart vehicles, human-robot interaction and content-based image filtration. However, human has been proven to be a much more difficult object to detect, because of the wide variability in appearance, due to clothing, articulation and illumination conditions that are not common in outdoor scenes. In this paper, based on the second generation Bandelet transform, we followed the geometric flow of images and presented a new feature extraction method to improve the accuracy of human detection in images.
Besides, many researches had been carried, focused on reducing time cost, improving the accuracy of region segmentation and enhancing the robust of detection. It involved the method of optical flow, image segmentation and the human detection method based on body-parts. Specifically, the paper proposed three original human detection methods based on second generation Bandelet transform.
1) Based on the second generation Bandelet transform, we followed the geometric flow of images and presented a new feature extraction method to improve the accuracy of human detection in images. Here the Bandelet coefficients and their statistical values were extracted as the features of human images, combined with linear SVM classifier, then we can classify and detect human in images.
2) Different from most of the previous work, we detect motion human by region segmentation and classification through machine learning. In our method, based on optical flow, region segmentation is carried firstly and then, based on geometric flow, Bandelet transform is used to do feature extraction and classification. Some treatments were carried after optical flow field computation to de-noising and some improvements in Bandelet transform were used to reduce time cost of feature extraction.
3) The third method focused on enhancing the robust of human detection by using body-parts and feature of Bandelet transform. Experiments showed that this algorithm could be an effective way to detection human in static or motion images, and it is worthy that we should do more effort into this in the future research.
引文
[1]林学闰,王宏等译.计算机视觉-一种现代方法.人民邮电出版社, 2004.
[2] Shapio, stockman著.计算机视觉.机械工业出版社, 2005.
[3]边肇祺,张学工等编著.模式识别.清华大学出版社, 2000.
[4] Navneet DALAL. Finding People in Images and Videos. Navneet DALAL'PhD dissertation. 2006, 6.
[5] D.M.Gavrila. The Visual Analysis of Human Movement: A Survey Computer Vision and Image Understanding. 1999, 1, 73(1). Pp: 82-98.
[6] Collins R,Lipton A, Kanade T. Introduction to the special section on video surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(8). Pp: 745-746.
[7] Thomas McKenna. Video surveillance and human activity recognition for anti-terrorism and force protection. IEEE Conference on Advanced Video and signal Based Surveillance. 2003. Pp: 2.
[8] Boghossian B,Velastin S. Image Processing system for Pedestrian monitoring using neural classification of normal motion patterns. Measurement and Control. 1999, 32(9). Pp: 261-246.
[9] Shashua A, Gdalyahu Y, Hayun G. Pedestrian detection for driving assistance systems: single-frame classification and system level performance. Proc. IEEE Intelligent Vehicles Symposium. Parma, Italy. 2004. Pp: 1-6.
[10] Sun Hui, Hua Cheng-Ying, Luo Yu-Pin. A multi-stage classifier based algorithm of pedestrian detection in night with a near infrared camera in a moving car. Proc. The 3rd IEEE International Conference on Image and Graphics. Hong Kong, China. 2004. Pp: 120-123.
[11]H.Lakany,G.Haycs,M.Hazlewood and S.Hillman. Human walking: Tracking and Analysis. Proc. IEE Colloquium on Motion Analysis and Tracking,Savoy Place,Lnodon. 1999, 5.
[12] RemiRonfard,Cordelia schmid and Bill Trigs. Learning to Parse Pictures of People. Euro Conference on Computer Vision. 2002.
[13] Mun Wai Lee and Isaac Cohen. Proposal Maps driven MCMC for Estimating Human Body Pose in static images. Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2004.
[14] Zhao Liang. Dressed Human Modeling, Detection, and Parts Localization [Ph.D. dissertation], CMU-RI-TR-01-19, Robotics Institute, Carnegie Mellon University, 2001.
[15] J. Shi and J.Malik. Normalized cuts and image segmentation. IEEE Transactions on Patter Analysis and Machine Intelligence. 2000, 22(8). Pp: 88-905.
[16] Pedro F. Felzenszwalb and Daniel P. H. Huttenlocher. Efficient Matching of Pictorial Structures. Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2000. Pp: 66-73.
[17] Anuj Mohan,Constantine Papageorgiou,and Tomaso Poggio,Example-Based object Detection in images by Components. IEEE Transactions on Pattern Analysis And Machine Intelligence. 2001, (23)4.
[18]Yazhou Liu, Shiguang Shan, Wenchao Zhang, Xilin Chen, Wen Gao,Granularity-tunable gradients partition (GGP) descriptors for human detection. IEEE Transactions on Digital Object Identifier. 2009, 6.
[19] Tate S, Takefuji Y. Video-based human shape detection by deformable templates and neural network. Proc. The 6th International Conference on Knowledge-Based Intelligent Information & Engineering Systems. Podere d’Ombriano, Crema, Italy: IOS Press. 2002. Pp: 280-285.
[20] Zhao Liang, Thorpe C. Stereo and neural network-based pedestrian detection. IEEE Transactions on Intelligent Transportation Systems. 2000, 1(3). Pp: 148-154.
[21] Szaras M, Yoshizawa A, Yamamoto M, Ogata J. Pedestrian detection with convolutional neural networks. Proc. IEEE Intelligent Vehicles Symposium. Las Vegas, Nevada. 2005. Pp: 224-229.
[22] Vapnik V. The Nature of Statistical Learning Theory. Berlin: Springer-Verlag. 1999.
[23] Oren M, Papageorgiou C, Sinha P. Pedestrian detection using wavelet templates. Proc. IEEE Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico. 1997. Pp: 193-199.
[24] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proc. IEEE Conference on Computer Vision and Pattern Recognition. Marriott, Hawaii. 2001, 1. Pp: 511-518.
[25] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision. 2004, 60(2). Pp: 91-110.
[26] N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. Proc. IEEE International Conference on Computer Vision and pattern Recognition. 2005.
[27] Q. Zhu, S. Avidan, M.-C. Yeh, and K.-T. Cheng. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. Proc. IEEE International Conference on Computer Vision and Pattern Recognition, 2006.
[28] H.-X. Jia, Y.-J. Zhang. Fast Human Detection by Boosting Histograms of Oriented Gradients. Proc. IEEE Fourth International Conference on Image and Graphics, 2007.
[29] N. Dalal, B. Triggs, and C. Schmid. Human Detection Using Oriented Histograms of Flow and Appearance. Proc. European Conference on Computer Vision, 2006.
[30] Sakrapee P, C.-H. Shen, J. Zhang. An Experimental Evaluation of Local Features for Pedestrian Classification. Proc. IEEE Digital Image Computing Techniques and Applications, 2007.
[31] Philip Geismann, Georg Schneider. A Two-staged Approach to Vision-based Pedestrian Recognition Using Haar and HOG Features. IEEE Intelligent Vehicles Symposium, 2008.
[32] Freund Y, Schapire R E. A Decision-theoretic Generalization of Online Learning and an Application to Boosting. Journal of Computer and System Sciences. 1997,55(1). Pp: 119.
[33] P.Viola, M.Jones. Rapid object detection using a boosted cascade of simple features. Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2001, 5. Pp: 11-18.
[34] B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, Pittsburgh, PA. 1992. Pp: 144-152.
[35] V. Vapnik, The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
[36] Li Zhang, Bo Wu and Ram Nevatia. Pedestrian Detection in Infrared Images based on Local Shape Features. IEEE Conference on Computer Vision and Pattern Recognition. 2007.
[37] Chih-Chung Chang and Chih-Jen Lin,LIBSVM: a library for support vector machines,2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html
[38] G. Peyréand S. Mallat. Second Generation Bandelets and their Application to Image and 3D Meshes Compression. (CMAP 2004). Mathematics and Image Analysis, in press.
[39] E. LE. Pennec and S. Mallat. Sparse Geometrical Image Representation with Bandelets. IEEE Transactions on Image Processing. 2005, 14(4). Pp: 423-438.
[40]焦李成,侯彪,王爽,刘芳.图像多尺度几何分析理论与应用——后小波分析理论与应用.西安:西安电子科技大学, 2008.
[41] G. Peyréand S. Mallat. Surface compression with geometric bandelets. ACM Transactions on Graphics (TOG). 2005, 7, 24(3). Pp: 601- 608.
[42] Shuyuan Yang, Fang Liu, Min Wang, Licheng Jiao. Multiscale bandelet image compression. IEEE Transactions on Digital Object Identifier. ISPACS 2007. Pp: 412–415.
[43] Xiaohui Yang, Wei Li and Licheng Jiao, Image Denoising Based on Second Generation Bandelets and Multi-level Thresholding. Proc. The 6th World Congress on Intelligent Control and Automation. 2006.
[44] INRIA Static Person Data Set: http://lear.inrialpes.fr/data.
[45] Haifeng Xu, Akmal A. Younis, Mansur R .Kabuka. Automatic Moving Object Extraction for Content- Based Applications. IEEE Transactions on Circuits and Systems for Video Technology. 2004, 6, 14(6). Pp:796-812.
[46] I.Haritaoglu, D.Harwood, L.S.Davis. W4: Who? When? Where? What?-A real time system for detecting and tracking people. International Conference on Automatic Face and Gesture Recognition, Nara, Japan,1998. Pp: 222-227.
[47] A.J.Lipton, H.Fujiyoshi, R.S.Patil. Moving Target Classification and Tracking from Real-Time Video. Proc. IEEE Workshop Applications of Computer Vision. 1998. Pp: 8-14.
[48] G.Kahne, S.Richter, M.Beier. Motion-Based Segmentation and Contour-Based Classification of Video Objects. ACM Multimedia. 2001, 10.
[49] G.P.Daniel, G.Chuang, M.T.Sun. Semantic Video Object Extraction UsingFour-Band Water Shed and Partition Lattice Operators. IEEE Transactions on Circuits and Systems for Video Technology. 2001, 5, 11(5). Pp: 603-618.
[50]黄友珍,黄艺,余兆明.基于修正分水岭算法和时域跟踪的视频自动分割[M].数字视频. 2000, 1. Pp:5-8.
[51] S.Nitsuwat, J.S.Jin, H.M.Hudson. Motion-Based Video Segmentation Using Fuzzy Clustering and Classical Mixture Model. Proceedings of the International Conference of Image Processing. 2000, 1. Pp: 300-303.
[52] Clement Fredembach, Michael Schroder, Sabine Susstrunk. Eigenregions for Image Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004, 12, 26(12). Pp: 1222-1229.
[53] Horn B.K.P, Schunck B.G Determining optical flow. Artificial Intelligence,1981,17. Pp :185-204.
[54] Temujin Gautama, Marc M. Van Hulle. A Phase-Based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering. IEEE Transactions on Neural Networks. 2002, 13(5). Pp: 1127-1136.
[55] Mohan A, Papageorgiou C, Poggio T. Example-Based Object Detection in Images by Components. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2001, 23(4). Pp: 349-361.
[56] WU B. Detection of Multiple Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors. IEEE International Conference on Computer Vision. 2005. Pp: 90-97.
[57] Mikolajczvk K, Schmid C, Zissenrman A. Human Detection Based on a Probabilistic Assembly of Robust Part Detector. ECCV 2004, 1. Pp: 69-82.
[58]田广,戚飞虎,朱文佳等.单目移动拍摄下基于人体部位的行人检测.系统仿真学报. 2006, 10, 18(10).
[59] Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. The Annals of Statistics. 2000, 28(2). Pp: 337-407.
[60] B. Wu and R. Nevatia. Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. IEEE Conference on Computer Vision and Pattern Recognition. 2008.
[2] Shapio, stockman著.计算机视觉.机械工业出版社, 2005.
[3]边肇祺,张学工等编著.模式识别.清华大学出版社, 2000.
[4] Navneet DALAL. Finding People in Images and Videos. Navneet DALAL'PhD dissertation. 2006, 6.
[5] D.M.Gavrila. The Visual Analysis of Human Movement: A Survey Computer Vision and Image Understanding. 1999, 1, 73(1). Pp: 82-98.
[6] Collins R,Lipton A, Kanade T. Introduction to the special section on video surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(8). Pp: 745-746.
[7] Thomas McKenna. Video surveillance and human activity recognition for anti-terrorism and force protection. IEEE Conference on Advanced Video and signal Based Surveillance. 2003. Pp: 2.
[8] Boghossian B,Velastin S. Image Processing system for Pedestrian monitoring using neural classification of normal motion patterns. Measurement and Control. 1999, 32(9). Pp: 261-246.
[9] Shashua A, Gdalyahu Y, Hayun G. Pedestrian detection for driving assistance systems: single-frame classification and system level performance. Proc. IEEE Intelligent Vehicles Symposium. Parma, Italy. 2004. Pp: 1-6.
[10] Sun Hui, Hua Cheng-Ying, Luo Yu-Pin. A multi-stage classifier based algorithm of pedestrian detection in night with a near infrared camera in a moving car. Proc. The 3rd IEEE International Conference on Image and Graphics. Hong Kong, China. 2004. Pp: 120-123.
[11]H.Lakany,G.Haycs,M.Hazlewood and S.Hillman. Human walking: Tracking and Analysis. Proc. IEE Colloquium on Motion Analysis and Tracking,Savoy Place,Lnodon. 1999, 5.
[12] RemiRonfard,Cordelia schmid and Bill Trigs. Learning to Parse Pictures of People. Euro Conference on Computer Vision. 2002.
[13] Mun Wai Lee and Isaac Cohen. Proposal Maps driven MCMC for Estimating Human Body Pose in static images. Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2004.
[14] Zhao Liang. Dressed Human Modeling, Detection, and Parts Localization [Ph.D. dissertation], CMU-RI-TR-01-19, Robotics Institute, Carnegie Mellon University, 2001.
[15] J. Shi and J.Malik. Normalized cuts and image segmentation. IEEE Transactions on Patter Analysis and Machine Intelligence. 2000, 22(8). Pp: 88-905.
[16] Pedro F. Felzenszwalb and Daniel P. H. Huttenlocher. Efficient Matching of Pictorial Structures. Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2000. Pp: 66-73.
[17] Anuj Mohan,Constantine Papageorgiou,and Tomaso Poggio,Example-Based object Detection in images by Components. IEEE Transactions on Pattern Analysis And Machine Intelligence. 2001, (23)4.
[18]Yazhou Liu, Shiguang Shan, Wenchao Zhang, Xilin Chen, Wen Gao,Granularity-tunable gradients partition (GGP) descriptors for human detection. IEEE Transactions on Digital Object Identifier. 2009, 6.
[19] Tate S, Takefuji Y. Video-based human shape detection by deformable templates and neural network. Proc. The 6th International Conference on Knowledge-Based Intelligent Information & Engineering Systems. Podere d’Ombriano, Crema, Italy: IOS Press. 2002. Pp: 280-285.
[20] Zhao Liang, Thorpe C. Stereo and neural network-based pedestrian detection. IEEE Transactions on Intelligent Transportation Systems. 2000, 1(3). Pp: 148-154.
[21] Szaras M, Yoshizawa A, Yamamoto M, Ogata J. Pedestrian detection with convolutional neural networks. Proc. IEEE Intelligent Vehicles Symposium. Las Vegas, Nevada. 2005. Pp: 224-229.
[22] Vapnik V. The Nature of Statistical Learning Theory. Berlin: Springer-Verlag. 1999.
[23] Oren M, Papageorgiou C, Sinha P. Pedestrian detection using wavelet templates. Proc. IEEE Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico. 1997. Pp: 193-199.
[24] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proc. IEEE Conference on Computer Vision and Pattern Recognition. Marriott, Hawaii. 2001, 1. Pp: 511-518.
[25] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision. 2004, 60(2). Pp: 91-110.
[26] N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. Proc. IEEE International Conference on Computer Vision and pattern Recognition. 2005.
[27] Q. Zhu, S. Avidan, M.-C. Yeh, and K.-T. Cheng. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. Proc. IEEE International Conference on Computer Vision and Pattern Recognition, 2006.
[28] H.-X. Jia, Y.-J. Zhang. Fast Human Detection by Boosting Histograms of Oriented Gradients. Proc. IEEE Fourth International Conference on Image and Graphics, 2007.
[29] N. Dalal, B. Triggs, and C. Schmid. Human Detection Using Oriented Histograms of Flow and Appearance. Proc. European Conference on Computer Vision, 2006.
[30] Sakrapee P, C.-H. Shen, J. Zhang. An Experimental Evaluation of Local Features for Pedestrian Classification. Proc. IEEE Digital Image Computing Techniques and Applications, 2007.
[31] Philip Geismann, Georg Schneider. A Two-staged Approach to Vision-based Pedestrian Recognition Using Haar and HOG Features. IEEE Intelligent Vehicles Symposium, 2008.
[32] Freund Y, Schapire R E. A Decision-theoretic Generalization of Online Learning and an Application to Boosting. Journal of Computer and System Sciences. 1997,55(1). Pp: 119.
[33] P.Viola, M.Jones. Rapid object detection using a boosted cascade of simple features. Proc. IEEE Conference on Computer Vision and Pattern Recognition. 2001, 5. Pp: 11-18.
[34] B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, Pittsburgh, PA. 1992. Pp: 144-152.
[35] V. Vapnik, The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
[36] Li Zhang, Bo Wu and Ram Nevatia. Pedestrian Detection in Infrared Images based on Local Shape Features. IEEE Conference on Computer Vision and Pattern Recognition. 2007.
[37] Chih-Chung Chang and Chih-Jen Lin,LIBSVM: a library for support vector machines,2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html
[38] G. Peyréand S. Mallat. Second Generation Bandelets and their Application to Image and 3D Meshes Compression. (CMAP 2004). Mathematics and Image Analysis, in press.
[39] E. LE. Pennec and S. Mallat. Sparse Geometrical Image Representation with Bandelets. IEEE Transactions on Image Processing. 2005, 14(4). Pp: 423-438.
[40]焦李成,侯彪,王爽,刘芳.图像多尺度几何分析理论与应用——后小波分析理论与应用.西安:西安电子科技大学, 2008.
[41] G. Peyréand S. Mallat. Surface compression with geometric bandelets. ACM Transactions on Graphics (TOG). 2005, 7, 24(3). Pp: 601- 608.
[42] Shuyuan Yang, Fang Liu, Min Wang, Licheng Jiao. Multiscale bandelet image compression. IEEE Transactions on Digital Object Identifier. ISPACS 2007. Pp: 412–415.
[43] Xiaohui Yang, Wei Li and Licheng Jiao, Image Denoising Based on Second Generation Bandelets and Multi-level Thresholding. Proc. The 6th World Congress on Intelligent Control and Automation. 2006.
[44] INRIA Static Person Data Set: http://lear.inrialpes.fr/data.
[45] Haifeng Xu, Akmal A. Younis, Mansur R .Kabuka. Automatic Moving Object Extraction for Content- Based Applications. IEEE Transactions on Circuits and Systems for Video Technology. 2004, 6, 14(6). Pp:796-812.
[46] I.Haritaoglu, D.Harwood, L.S.Davis. W4: Who? When? Where? What?-A real time system for detecting and tracking people. International Conference on Automatic Face and Gesture Recognition, Nara, Japan,1998. Pp: 222-227.
[47] A.J.Lipton, H.Fujiyoshi, R.S.Patil. Moving Target Classification and Tracking from Real-Time Video. Proc. IEEE Workshop Applications of Computer Vision. 1998. Pp: 8-14.
[48] G.Kahne, S.Richter, M.Beier. Motion-Based Segmentation and Contour-Based Classification of Video Objects. ACM Multimedia. 2001, 10.
[49] G.P.Daniel, G.Chuang, M.T.Sun. Semantic Video Object Extraction UsingFour-Band Water Shed and Partition Lattice Operators. IEEE Transactions on Circuits and Systems for Video Technology. 2001, 5, 11(5). Pp: 603-618.
[50]黄友珍,黄艺,余兆明.基于修正分水岭算法和时域跟踪的视频自动分割[M].数字视频. 2000, 1. Pp:5-8.
[51] S.Nitsuwat, J.S.Jin, H.M.Hudson. Motion-Based Video Segmentation Using Fuzzy Clustering and Classical Mixture Model. Proceedings of the International Conference of Image Processing. 2000, 1. Pp: 300-303.
[52] Clement Fredembach, Michael Schroder, Sabine Susstrunk. Eigenregions for Image Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004, 12, 26(12). Pp: 1222-1229.
[53] Horn B.K.P, Schunck B.G Determining optical flow. Artificial Intelligence,1981,17. Pp :185-204.
[54] Temujin Gautama, Marc M. Van Hulle. A Phase-Based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering. IEEE Transactions on Neural Networks. 2002, 13(5). Pp: 1127-1136.
[55] Mohan A, Papageorgiou C, Poggio T. Example-Based Object Detection in Images by Components. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2001, 23(4). Pp: 349-361.
[56] WU B. Detection of Multiple Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors. IEEE International Conference on Computer Vision. 2005. Pp: 90-97.
[57] Mikolajczvk K, Schmid C, Zissenrman A. Human Detection Based on a Probabilistic Assembly of Robust Part Detector. ECCV 2004, 1. Pp: 69-82.
[58]田广,戚飞虎,朱文佳等.单目移动拍摄下基于人体部位的行人检测.系统仿真学报. 2006, 10, 18(10).
[59] Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. The Annals of Statistics. 2000, 28(2). Pp: 337-407.
[60] B. Wu and R. Nevatia. Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. IEEE Conference on Computer Vision and Pattern Recognition. 2008.