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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
dc.contributor.author | Yu-Chun Lin | en |
dc.contributor.author | 林預淳 | zh_TW |
dc.date.accessioned | 2021-06-15T07:07:00Z | - |
dc.date.available | 2012-12-10 | |
dc.date.copyright | 2010-12-10 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-11-18 | |
dc.identifier.citation | [1] J. Li, 'Intensity-Distance Projection Space Based Human Tracking in Far-Infrared Image Sequences,' 2009, pp. 371-375.
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Chongzhao, 'Night-time pedestrian detection by visual-infrared video fusion,' in 7th World Congress on Intelligent Control and Automation., 2008, pp. 5079-5084. [13] R. Arndt, et al., 'Detection and Tracking of Multiple Pedestrians in Automotive Applications,' in IEEE Intelligent Vehicles Symposium, 2007, pp. 13-18. [14] M. Hussein, et al., 'A comprehensive evaluation framework and a comparative study for human detectors,' Trans. Intell. Transport. Sys., vol. 10, pp. 417-427, 2009. [15] M. Soga, et al., 'Pedestrian Detection for a Near Infrared Imaging System,' in 11th International IEEE Conference on Intelligent Transportation Systems., 2008, pp. 1167-1172. [16] J. Ge, et al., 'Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems,' IEEE Transactions on Intelligent Transportation Systems vol. 10, pp. 283-298, 2009. [17] R. Lienhart and J. Maydt, 'An extended set of Haar-like features for rapid object detection,' in International Conference on Image Processing Proceedings. , 2002, pp. I-900-I-903 vol.1. [18] P. Sabzmeydani and G. Mori, 'Detecting Pedestrians by Learning Shapelet Features,' in IEEE Conference on Computer Vision and Pattern Recognition., 2007, pp. 1-8. [19] V. N. Vapnik, The nature of statistical learning theory: Springer-Verlag New York, Inc., 1995. [20] Y. Freund and R. E. Schapire, 'A decision-theoretic generalization of on-line learning and an application to boosting,' J. Comput. Syst. Sci., vol. 55, pp. 119-139, 1997. [21] J. Dong, et al., 'Nighttime Pedestrian Detection with Near Infrared using Cascaded Classifiers,' in IEEE International Conference on Image Processing., 2007, pp. VI - 185-VI - 188. [22] T. B. Moeslund, 'A survey of advances in vision-based human motion capture and analysis,' Computer Vision and Image Understanding, vol. 104, p. 90, 2006. [23] H. 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Brogefors, 'Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, pp. 849-865, 1988. [34] M. Enzweiler and D. M. Gavrila, 'Monocular Pedestrian Detection: Survey and Experiments,' IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 31, pp. 2179-2195, 2009. [35] P. Gehler and S. Nowozin, 'On Feature Combination for Multiclass Object Classification,' in ICCV, 2009. [36] C.-C. Chang and C.-J. Lin. (2001, LIBSVM : a library for support vector machines. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/48659 | - |
dc.description.abstract | 在電腦視覺的領域中,行人偵測是個重要的主題,而且其中夜間行人偵測更是特別的困難且有挑戰性。在這篇論文中,我們將偵測行人的問題定義於夜間的移動式平台使用相機所擷取的影像串流上。目前大多數的夜間行人偵測方法都只是使用圖像上的單一特徵為其核心。原本於日間的環境下使用很多有效的圖像特徵,換到夜間的環境時遭遇到了缺少材質、高對比以及低照度的問題。在這樣的問題條件下,我們首先使用智慧區域偵測方法切割出圖像中的前景以便產生出候選區域。在這之後我們設計一個夜間行人偵測系統基於AdaBoost以及支持向量機分類器,結合輪廓以及旋轉強度之方向長條圖特徵以達到有效的將候選區域辨識為行人與否。結合不一樣類型且互補的特徵可以增加偵測的效果。實驗結果展現出我們的行人偵測系統於夜間環境中達到所期望的成果。 | zh_TW |
dc.description.abstract | Pedestrian detection is an important subject in computer vision field, and the nighttime pedestrian detection is especially difficult and challenging. In this thesis, we address the problem of detecting pedestrians in video streams from a moving camera at nighttime. Most nighttime human detection approaches only use single feature extracted from images. The effective image features in daytime environment may suffer from textureless, high contrast and low light problems at night. To deal with these issues, we first segment the foreground by using the proposed Smart Region Detection approach to generate candidates. Then we design a nighttime pedestrian detection system based on the AdaBoost and support vector machine (SVM) classifiers with contour and histogram of oriented gradients (HOG) features to effectively recognize pedestrians from those candidates. Combining different type of complementary features can improve the detection performance. Experiment results show that our pedestrian detection system is promising in the nighttime environment. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T07:07:00Z (GMT). No. of bitstreams: 1 ntu-99-R97922078-1.pdf: 1410686 bytes, checksum: b91714c243714e15cba2fb6a26d67961 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Challenges 3 1.3 Related Work 6 1.4 Contributions 9 1.5 Organization 11 Chapter 2 Preliminaries 13 2.1 Problem Definition 13 2.2 Support Vector Machine (SVM) 15 2.2.1 Objective of SVM 15 2.2.2 Overview of SVM 16 2.3 AdaBoost Algorithm 19 2.3.1 Objective of AdaBoost 20 2.3.2 Overview of AdaBoost 20 2.4 Feature Combination Methods 22 2.4.1 Feature Combination Problem 23 2.4.2 Kernel Methods 23 2.5 Overview of Proposed System 25 Chapter 3 Human Candidate Generation 26 3.1 Typical Segmentation Approach in Nighttime 26 3.1.1 Intensity-based Segmentation 27 3.1.2 Haar-like Feature Detection 29 3.2 Smart Region Detection and Thresholding 30 3.2.1 Local Thresholding Approach 31 3.2.2 Smart Region-Based Image Binarization Algorithm 32 Chapter 4 Human Candidate Verification by Multiple Features 37 4.1 Feature Descriptors 37 4.1.1 Histogram of Oriented Gradients (HOG) 38 4.1.2 Contour-based Feature 38 4.2 Feature Combination 41 4.2.1 Baseline Methods 42 4.2.2 The Proposed Feature Combination Approach by SVM 43 4.3 Human Candidate Detection 46 4.3.1 Training Stage 46 4.3.2 Classification Stage 48 Chapter 5 Experiment 49 5.1 Environment Description and Database 49 5.1.1 Environment Description 49 5.1.2 Database 51 5.2 Experiment Results and Performance Evaluation 52 5.2.1 Training Phase Result 52 5.2.2 Detection Phase Result 54 5.2.3 System Processing Time 55 5.3 Comparison of Each Feature and Combined 56 5.4 Comparison of Each Segmentation Approach 59 Chapter 6 Conclusion 60 References 62 | |
dc.language.iso | en | |
dc.title | 結合多種特徵之近紅外線夜間行人偵測 | zh_TW |
dc.title | Near-Infrared Based Nighttime Pedestrian Detection by Combining Multiple Features | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 蕭培墉(Pei-Yung Hsiao) | |
dc.contributor.oralexamcommittee | 黃世勳(Shih-Shinh Huang),陳祝嵩(Chu-Song Chen) | |
dc.subject.keyword | 夜間,行人偵測,輪廓,方向梯度直方圖,支持向量機, | zh_TW |
dc.subject.keyword | nighttime,pedestrian detection,contour,HOG,SVM, | en |
dc.relation.page | 65 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-11-18 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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