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Description of vehicle passage through a multisegment detection field

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents the video-based description method for vehicles passing a detection field. A sequence of source images is created by consecutive frames of the input video stream. The source images are converted into binary target images using the analysis of small gradients. Binary values of the target images represent edges and surfaces comprised in the source images. For all images, the same detection field composed of segments is defined. Inside each segment of the detection field, the sum of edge values is calculated. For the entire detection field, an adjusted sum of the edge values is determined. A vehicle passing the detection field changes the number of edge values within individual segments and the adjusted sum of the edge values for the entire detection field. Vehicle passage through the detection field is described by a discrete function that associates the adjusted sum of the edge values determined for the entire detection field in the current binary image to the ordinal number of the current image in the sequence of source images.
Rocznik
Tom
Strony
41--50
Opis fizyczny
Bibliogr. 11 poz.
Twórcy
  • Faculty of Transport, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland
Bibliografia
  • 1. Coifman Benjamin, David Beymer, Philip McLauchlan, Jitendra Malik. 1998. “A realtime computer vision system for vehicle tracking and traffic surveillance”. Transportation Research Part C 6(4): 271-288. DOI: 10.1016/s0968-090x(98)00019-9.
  • 2. Cucchiara Rita, Piccardi Massimo, Mello Paola. 2000. “Image Analysis and Rule-Based Reasoning for a Traffic Monitoring System”. IEEE Transaction on Intelligent Transaction Systems 1(2): 119-130. DOI: 10.1109/6979.8809.69.
  • 3. Fernandez-Caballero Antonio, Francisco J. Gomez, Juan Lopez-Lopez. 2008. “Road traffic monitoring by knowledge-driven static and dynamic image analysis”. Expert Systems with Applications 35(3): 701-719. DOI: 10.1016/j.eswa.2007.07.017.
  • 4. Gupte Surendra, Osama Masoud, Robert F.K. Martin, Nikolaos P. Papanikolopoulos. 2002. “Detection and Classification of Vehicles”. IEEE Transaction on Intelligent Transportation Systems 3(1): 37-47. DOI: 10.1109/6979.994794.
  • 5. Hsieh Jun Wei, Shih-Hao Yu, Jung-Sheng Chen, Wen-Fong Hu. 2006. „Automatic Traffic Surveillance System for Vehicle Tracking and Classification”. IEEE Transaction on Intelligent Transportation Systems 7(2): 175-187. DOI: 10.1109/tits.2006.874722.
  • 6. Jarašūniene Aldona. 2007. “Research into intelligent transport systems (ITS) technologies and efficiency”. Transport 22(2): 61-67. DOI: 10.1080/16484142.2007.9638100.
  • 7. Joonho Ko, Daejin Kim, Heung Gweon Sin & Seungjae Lee. 2014. “The efficiency of vehicle monitoring locations for a voluntary travel demand management program”. Transport 29(3): 326-333. DOI: 10.3846/16484142.2014.953206.
  • 8. Kamijo Shunsuke, Yasuyuki Matsushita, Katsushi Ikeuchi, Masao Sakauchi. 2000. “Traffic Monitoring and Accident Detection at Intersections”. IEEE Transactions on Intelligence Transportation Systems 1(2): 108-118. DOI: 10.1109/6979.880968.
  • 9. Mithun Niluthpol Chowdhury, Nafi Ur Rashid, S.M. Mahbubur Rahman. 2012. “Detection and Classification of Vehicles From Video Using Multiple Time-Spatial Images”. IEEE Transaction on Intelligent Transportation Systems 13(3): 1215-1225. DOI: 10.1109/tits.2012.2186128.
  • 10. Niu Hongxia, Tao Hou. 2018. “Fast detection study of foreign object intrusion on railway track”. Archives of Transport 47(3): 79-89. ISSN 0866-9546.
  • 11. Stauffer Chris, W.E.L. Grimson. 1999. “Adaptive background mixture models for real time tracking”. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2: 246-252. IEEE. 23-25 June 1999, Fort Collins, CO, USA. ISBN: 0-7695-0149-4. DOI: 10.1109/cvpr.1999.784637.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2d08aefe-5680-44ab-b7c3-0602e926631b
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