Classification of road traffic conditions based on texture features of traffic images using neural networks

• Autorzy: Pamuła T.

Identyfikatory

DOI

10.20858/sjsutst.2016.92.10

• Języki publikacji: EN

Abstrakty

EN

The paper presents a method of classification of road traffic conditions based on the analysis of the content of images of the traffic flow. The view of the traffic lanes with vehicles is treated as a texture, while the change in the description of its characteristics is ascribed to the change in the density of traffic. Four classes of conditions are determined on the basis of the values of Haralick texture features. An MLP network is used for classification. Video data, which were registered by an UAV hanging over a traffic junction, are used for validation of the method.

Słowa kluczowe

EN

traffic conditions; texture; neural networks

PL

warunki drogowe; tekstura; sieci neuronowe

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2016
• Tom: z. 92
• Strony: 101--109
• Opis fizyczny: Bibliogr. 9 poz.

Twórcy

autor

Pamuła T.

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

Bibliografia

• 1. Bi Song, Han Li-qun, Zhong Yi-xin, Wang Xiao-jie. 2011. “All-day traffic states recognition system without vehicle segmentation”. Journal of China Universities of Posts and Telecommunications 18: 1-11.
• 2. Li Weia, Dai Hong-yingb. 2016. “Real-time road congestion detection based on image texture analysis”. Procedia Engineering 137: 196-201.
• 3. Tracz Marian, Janusz Chodur. 2004. Metoda obliczania przepustowości skrzyżowań z sygnalizacją świetlną. Warsaw: GDDKiA. [In Polish: The Method of Calculating the Capacity of Intersections with Traffic Lights. Warsaw: GDDKiA].
• 4. Kalaitzakis Kostas, V. Kastrinaki, Michalis Zervakis. 2003. “A survey of video processing techniques for traffic applications”. Image and Vision Computing 21: 359-381.
• 5. Xiying Lia, Yongye Shea, Donghua Luob, Zhi Yu. 2013. “A traffic state detection tool for freeway video surveillance system”. In 13th COTA International Conference of Transportation Professionals. Procedia, Social and Behavioral Sciences: 2453-2461.
• 6. Yu Qiaoa, Zhongke Shia. 2012. “Traffic parameters detection using edge and texture”. Procedia Engineering 29: 3858-3862.
• 7. Haralick Robert M., K. Shanmugam, Its’Hak Dinstein. 1973. “Textural features for image classification”. IEEE Transaction on Systems, Man and Cybernetics 3(6): 610-621.
• 8. Osowski Stanisław. 1996. Sieci neuronowe w ujęci algorytmicznym. Warsaw: WNT. [In Polish: Neural networks in terms of algorithmic. Warsaw: WNT].
• 9. Pamuła Teresa. 2011. “Road traffic parameters prediction in urban traffic management systems using neural networks”. Transport Problems 6(3): 123-129.

Uwagi

PL

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