傅立成臺灣大學:資訊工程學研究所李忞蔚Li, Min-WeiMin-WeiLi2010-05-172018-07-052010-05-172018-07-052009U0001-0508200902500000http://ntur.lib.ntu.edu.tw//handle/246246/183394Pedestrian detection in video streams from a moving camera at nighttime is important for many applications. In this thesis, we present a nighttime pedestrian detection system that is based on the AdaBoost classifier with the symmetry weighted shapelet feature. There are three stages in the proposed system: a) find human candidates by segmentation with an adaptive thresholding method b) reject unreasonable candidates by some geometric constraints, such as the aspect ratio and the size of a candidate region, and c) verify the candidates by an AdaBoost classifier with symmetry weighted shapelet. The symmetry weighted shapelet is an enhanced version of the shapelet feature. We improve the shapelet feature to make it more informative for encoding pedestrians in a near-infrared image by embedding human symmetry property. The symmetry property is imposed on shapelet features by computing the similarity between itself and its symmetric pair to weight shapelet features. We also design a new feature evaluation approach, namely, Discrimination Measure Model (DMM), and by employing this approach we can more efficiently evaluate the discriminating power of the symmetry weighted shapelet, and then treat it as an additional support evidence for the proposed system.n the experiments, our proposed nighttime pedestrian detection system demonstrates reliable results for pedestrian classification and the symmetry weighted shapelet also present a larger discriminating power than Histogram of Oriented Gradients (HOG) and original shapelet feature.口試委員會審定書 ....................................... #謝 ................................................... i文摘要 .............................................. iiiBSTRACT ............................................... ivONTENTS ................................................ vIST OF FIGURES ...................................... viiiIST OF TABLES .......................................... xhapter 1 Introduction .................................. 1.1 Motivation .......................................... 2.2 Challenges .......................................... 3.3 Related Work ........................................ 7.4 Contributions ....................................... 11.5 Organization ........................................ 12hapter 2 Problem Definition and Preliminaries .......... 14.1 Problem Description ................................. 14.2 Support Vector Machine (SVM) ........................ 16.2.1 Objective of SVM .................................. 16.2.2 Overview of SVM ................................... 17.3 AdaBoost Algorithm .................................. 20.3.1 Objective of AdaBoost ............................. 20.3.2 Overview of AdaBoost .............................. 21.4 Overview of Proposed System ......................... 23hapter 3 Feature Evaluation ............................ 25.1 Discrimination Measure Model ........................ 25.1.1 Classification Problem ............................ 26.1.2 Separability ...................................... 27.1.3 Concentration ..................................... 29.2 Integration with SVM ................................ 30.3 Selection of Discriminative Feature ................. 32hapter 4 Nighttime Pedestrian Detection ................ 34.1 Human Candidate Generation .......................... 35.2 Feature Descriptors ................................. 36.2.1 Histogram of Oriented Gradients ................... 36.2.2 Shapelet .......................................... 38.2.3 Symmetry Weighted Shapelet ........................ 42.3 Human Candidate Verification ........................ 46.3.1Training............................................ 46.3.2 Classification .................................... 47.4 Discussion of Near-Infrared based Nighttime Pedestrian Detection ............................................... 47hapter 5 Experiment .................................... 49.1 Environment Description ............................. 49.2 Database............................................. 50.3 Training............................................. 51.3.1 Database Parameter ................................ 52.3.2 Feature Descriptor Parameter ...................... 52.4 Experiment Results .................................. 53hapter 6 Conclusion .................................... 60eferences .............................................. 62application/pdf1626518 bytesapplication/pdfen-US行人人形偵測特徵評估夜間PedestrianHumanDetectionNighttimeFeature EvaluationShapeletthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/183394/1/ntu-98-R96922127-1.pdf