政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49471

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/49471

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题名:	應用共變異矩陣描述子及半監督式學習於行人偵測 Semi-supervised learning for pedestrian detection with covariance matrix feature
作者:	黃靈威 Huang, Ling Wei
贡献者:	廖文宏 Liao, Wen Hung 黃靈威 Huang, Ling Wei
关键词:	半監督式學習支持向量機單純貝氏分類器共變異描述子 Semi-supervised learning Support vector machine Naïve Bayes classifier Covariance descriptor
日期:	2008
上传时间:	2010-12-08 12:06:40 (UTC+8)
摘要:	行人偵測為物件偵測領域中一個極具挑戰性的議題。其主要問題在於人體姿勢以及衣著服飾的多變性，加之以光源照射狀況迥異，大幅增加了辨識的困難度。吾人在本論文中提出利用共變異矩陣描述子及結合單純貝氏分類器與級聯支持向量機的線上學習辨識器，以增進行人辨識之正確率與重現率。實驗結果顯示，本論文所提出之線上學習策略在某些辨識狀況較差之資料集中能有效提升正確率與重現率達百分之十四。此外，即便於相同之初始訓練條件下，在USC Pedestrian Detection Test Set、 INRIA Person dataset 及 Penn-Fudan Database for Pedestrian Detection and Segmentation三個資料集中，本研究之正確率與重現率亦較HOG搭配AdaBoost之行人辨識方式為優。 Pedestrian detection is an important yet challenging problem in object classification due to flexible body pose, loose clothing and ever-changing illumination. In this thesis, we employ covariance feature and propose an on-line learning classifier which combines naïve Bayes classifier and cascade support vector machine (SVM) to improve the precision and recall rate of pedestrian detection in a still image. Experimental results show that our on-line learning strategy can improve precision and recall rate about 14% in some difficult situations. Furthermore, even under the same initial training condition, our method outperforms HOG + AdaBoost in USC Pedestrian Detection Test Set, INRIA Person dataset and Penn-Fudan Database for Pedestrian Detection and Segmentation.
參考文獻:	[1] IBM Smart Surveillance System http://www.research.ibm.com/peoplevision/ [2] オムロン株式会社公開特許公報【移動体検出方法及び装置並びに移動体認識方法及び装置並びに人間検出方法及び装置】日本国特許庁,1999 [3] Oncel Tuzel, Fatih Porikli, and Peter Meer, “Human detection via classification on Riemannian manifolds”, IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007. [4] Arsigny Vincent, Fillard Pierre, Pennec Xavier, Ayache Nicholas, “Geometric means in a novel vector space structure on symmetric positive-definite matrices”, SIAM Journal on Matrix Analysis and Applications, Vol. 29(1), pp. 328–347, 2006. [5] Wolfgang Förstner, Boudewijn Moonen, “A metric for covariance matrices”, Technical report, Stuttgart University, Dept. of Geodesy and Geoinformatics, 1999. [6] Christopher Wren, Ali Azarbayejani, Trevor Darrell, Alex Pentland, “Pfinder: real-time tracking of the human body”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19(7), pp. 780–785, 1997. [7] Csaba Beleznai , Bernhard Fruhstuck, Horst Bischof, “Human detection in groups using a fast mean shift procedure”, International Conference on Image Processing, Vol. 1, pp. 349–352, 2004. [8] Tetsuji Haga, Kazuhiko Sumi, Yasushi Yagi, “Human detection in outdoor scene using spatio-temporal motion analysis”, Proceedings of the 17th International Conference on Pattern Recognition, Vol. 4, pp. 331–334, 2004. [9] How-Lung Eng, Junxian Wang, Alvin H. Kam, Wei-Yun Yau, “A Bayesian framework for robust human detection and occlusion handling using human shape model”, Proceedings of the 17th International Conference on Pattern Recognition, pp. 257 – 260, 2004. [10] Constantine P. Papageorgiou, Michael Oren, Tomaso Poggio, “A general framework for object detection”, Proceedings of the 6th International Conference on Computer Vision, pp. 555–562,1998. [11] Paul Viola, Michael J. Jones, Daniel Snow, “Detecting pedestrians using patterns of motion and appearance”, Proceedings of the 9th International Conference on Computer Vision, Vol. 2, pp. 734–741, 2003. [12] Navneet Dalal, Bill Triggs, “Histograms of oriented gradients for human detection”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893,2005. [13] Fatih Porikli, Oncel Tuzel, Peter Meer, “Covariance tracking using model update based on lie algebra” , Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,, pp. 728–735, 2006. [14] Shumeet Baluja, “Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data”, Neural Information Processing Systems, pp. 854–860, 1998. [15] Kanal Paul Nigam, “Using unlabeled data to improve text classification (Technical Report CMU-CS-01-126)”, Carnegie Mellon University. Doctoral Dissertation, pp.27 2001. [16] Alex D. Holub, Pietro Perona, Max Welling, “Exploiting unlabeled data for hybrid object classification”, NIPS Workshop in Inter-Class Transfer, 2005. [17] Yuanqing Li, Cuntai Guan, Huiqi Li, Zhengyang Chin, “A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system”, Pattern Recognition Letters, Vol. 29(9), pp. 1285-1294, 2008. [18] Raghav Subbarao, Peter Meer, “Nonlinear mean shift for clustering over analytic manifolds”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1168–1175, 2006. [19] Fatih Porikli, Tekin Kocak, "Robust license plate detection using covariance descriptor in a neural network framework," IEEE International Conference on Advanced Video and Signal Based Surveillance, pp.107, 2006 [20] Oncel Tuzel, Fatih Porikli, Peter Meer, “Region covariance: A fast descriptor for detection and classification”, Proceedings of the 9th European Conference on Computer Vision, Vol. 2, pp. 589–600, 2006. [21] Robert Gilmore, “Lie groups, Lie algebras, and some of their applications” , pp. 77 ,Dover, 2002 [22] Xavier Pennec, Pierre Fillard, Nicholas Ayache, ”A Riemannian framework for tensor computing”, International Journal of Computer Vision, pp.41-66, 2006. [23] Jonathan H. Manton, “A centroid (Karcher mean) approach to the joint approximate diagonalisation problem: The real symmetric case”, Digital Signal Processing, Vol.16, pp. 468-478, 2006. [24] Chui-Yu Chiu, Yuan-Ting Huang. “Integration of support vector machine with naïve Bayesian classifier for spam classification”, Fuzzy Systems and Knowledge Discovery 4th International Conference, Vol. 1, pp. 24-27, 2007. [25] http://cbcl.mit.edu/software-datasets/PedestrianData.html [26] http://www.science.uva.nl/research/isla/downloads/pedestrians/index.html [27] http://pascal.inrialpes.fr/data/human/ [28] http://iris.usc.edu/~bowu/DatasetWebpage/dataset.html [29] http://www.cis.upenn.edu/~jshi/ped_html/. [30] Ivan Laptev, "Improvements of object detection using boosted histograms", Proceedings of the 17th British Machine Vision Conference, pp. III:949-958, 2006. http://www.irisa.fr/vista/Equipe/People/Laptev/download.html [31] http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2006/index.html [32] Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang, ”An experimental evaluation of local features for pedestrian classification”, Digital Image Computing Techniques and Applications, 9th Biennial Conference of the Australian Pattern Recognition Society, pp.53-60, 2007
描述:	碩士國立政治大學資訊科學學系 96971006 97
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0096971006
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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