Video-based vehicle detection on a two-way road

• Autorzy: Czapla Z.

Identyfikatory

DOI

10.20858/sjsutst.2016.92.3

• Języki publikacji: EN

Abstrakty

EN

The paper presents a method of vehicle detection on a two-way road. Vehicle detection is carried out on the basis of the video stream from the camera placed over a road. The input image sequence is created by consecutive frames taken from the video stream. Images from the input image sequence are processed individually one by one. A detection field is defined for each lane of the road. Images from the input image sequence are converted into point image representation. The sums of the edge points within the detection fields are calculated. States of the detection fields are determined on the basis of calculated sums of the edge points. Vehicles are detected by analysis of states of the detection fields. Experimental results are provided.

Słowa kluczowe

EN

vehicle detection; two-way road; image processing

PL

detekcja pojazdów; droga dwukierunkowa; przetwarzanie obrazu

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2016
• Tom: z. 92
• Strony: 23--29
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Czapla Z.

• Faculty of Transport, Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland

Bibliografia

• 1. Coifman Benjamin, David Beymer, Philip McLauchlan, Jitendra Malik. 1998. “A realtime vision system for vehicle tracking and traffic surveillance”. Transportation Research Part C 6(4): 271-288.
• 2. Czapla Zbigniew. 2014. “Video based vehicle counting for multilane roads”. Logistyka (4): 2709-2717. ISSN 1231-5478.
• 3. Czapla Zbigniew. 2016. “Point image representation for efficient detection of vehicles”. In Proceedings of the Ninth International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing (403): 691-700. Springer International Publishing. ISBN 978-3-319-26225-3.
• 4. Di Zeno Silvano. 1986. “A note on the gradient of a multi-image”. Computer Vision, Graphics, and Image Processing 33(1): 116-125.
• 5. 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.
• 6. 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.
• 7. 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.
• 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.
• 9. Kang Chung-Chia, Wen-June Wang. 2007. “A novel edge detection method based on the maximizing objective function”. Pattern Recognition (40)2: 609-618.
• 10. 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
• 11. Krishnan R. Muthu, Miyilsamy Radha. 2011. “Edge detection techniques for image segmentation”. International Journal of Computer Science and Information Technology 3(6): 259-267.
• 12. Qian Richard J., Thomas S. Huang. 1996. “Optimal edge detection in two-dimensional images”. IEEE Transactions on Image Processing 5(7): 1215-1220.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę