English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90069/105176 (86%)
Visitors : 6603441      Online Users : 594
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    ASIA unversity > 資訊學院 > 會議論文 >  Item 310904400/7148

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/7148

    Title: Lane Change Detection and its Application to Suspicious Driving Behavior Analysis
    Authors: Sin-Yu Chen;Jun-Wei Hsieh
    Contributors: Department of Electrical Engineering, Yuan Ze University
    Date: 2007-12-20
    Issue Date: 2010-01-12 16:23:29 (UTC+8)
    Publisher: 亞洲大學資訊學院;中華電腦學會
    Abstract: This paper presents a novel edge labeling scheme for detecting lanes from videos in real time. Firstly, pairs of edge pixels with different edge types are grouped using the labeling technique. Then, different lane hypotheses can be generated for lane modeling. Then, a lane geometrical constraint is derived from the pinhole camera geometry for filtering out impossible lane hypotheses. Since the constraint is invariant to shadows and lighting changes, each desired lane can be robustly detected even though different occlusions and shadows are included in the analyzed scenes.After filtering, a kernel-based modeling technique is then proposed for modeling different lane properties. With the modeling, different lanes can be effectively detected and tracked even though they are fragmented into pieces of segments or occluded by shadows. The proposed scheme works very well to analyze lane conditions with night vision. With the lane information, different dangerous driving behaviors like lane departure can be directly analyzed from road scenes. Experimental results show that the proposed scheme is powerful in lane detection. The average accuracy rate of vehicle detection is 95%.
    Relation: 2007NCS全國計算機會議 12-20~21
    Appears in Collections:[資訊學院] 會議論文

    Files in This Item:

    File SizeFormat
    4106.pdf184KbAdobe PDF163View/Open

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

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback