English  |  正體中文  |  简体中文  |  Items with full text/Total items : 27221/39064
Visitors : 2403545      Online Users : 76
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44059

Title: Pedestrian Segmentation Using Deformable Triangulation and Kernel Density Estimation
Authors: Jun-Wei Hsieh
Sin-Yu Chen
Chi-Hung Chuang
Yung-Sheng Chen
Zhong-Yi Guo
Kuo-Chin Fan
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Kernel
Image segmentation
Image edge detection
Object detection
Cameras
Detectors
Video compression
Machine learning
Cybernetics
Object segmentation
Date: 2009-07
Issue Date: 2017-11-13T08:11:28Z
Publisher: Proc. of 22th IPPR Conference on Computer Vision, Graphics, and Image Processing
Abstract: Abstract:This paper proposes a novel kernel-based and technique to segment pedestrians from a single image. An important concept introduced in this paper is “detection before segmentation” for extracting pedestrians' boundaries more precisely no matter what cameras (mobile, PTZ, or stationary) are used or how does the background include various lighting changes. First of all, the Adaboost-based detector is trained for detecting all possible pedestrians from still images. Then, we adopt the Watershed algorithm to over-segment each frame as a rough segmentation. Since two homogenous regions will still connect together, a triangulation-based scheme is then used to divide them into different tinier regions using their edge features. Then, we propose a novel kernel density analysis to estimate the probability of each tinier region to be foreground or background. With the kernel modeling, an optimal segmentation of pedestrian can be found by maximizing a posteriori probability for maintaining the visual and spatial consistencies between each segmented regions. Then, each desired pedestrian can be more accurately extracted for content analysis even though it is occluded with other objects or captured by a mobile camera. Experimental results have shown the effectiveness and superiority of the proposed method in pedestrian segmentation.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44059
Appears in Collections:[資訊工程學系] 演講及研討會

Files in This Item:

File Description SizeFormat
index.html0KbHTML37View/Open


All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback