Gradient-based vehicle detection using a two-segment detection field.

• Autorzy: Czapla Z.

Identyfikatory

DOI

10.20858/sjsutst.2017.96.3

• Języki publikacji: EN

Abstrakty

EN

This paper presents a method of vehicle detection using the conversion of images from a source image sequence into binary target images. This conversion is performed on the basis of small image gradients. The location of binary values after conversion is in accordance with the edges of the converted image. For all processed images, a detection field is defined, which is composed of two segments. In the area of each segment, the sum of the edge values is calculated. On the basis of the calculated sums within the segments, an adjusted sum of the edge values is established, which allows for the determination of the state of the detection field. Vehicle detection is carried out by recognition of distinctive changes in the state of the detection field caused by the passing vehicle. Experimental results are provided.

Słowa kluczowe

EN

vehicle detection; image conversion; detection field

PL

detekcja pojazdów; konwersja obrazów; pole detekcji

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2017
• Tom: z. 96
• Strony: 27--36
• Opis fizyczny: Bibliogr. 7 poz.

Twórcy

autor

Czapla Z.

• 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. 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.
• 7. 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.