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Development of a Stop-Line Violation Detection System for Indian Vehicles

Development of a Stop-Line Violation Detection System for Indian Vehicles

Satadal Saha, Subhadip Basu, Mita Nasipuri
Copyright: © 2013 |Pages: 28
ISBN13: 9781466625181|ISBN10: 146662518X|EISBN13: 9781466625198
DOI: 10.4018/978-1-4666-2518-1.ch008
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MLA

Saha, Satadal, et al. "Development of a Stop-Line Violation Detection System for Indian Vehicles." Handbook of Research on Computational Intelligence for Engineering, Science, and Business, edited by Siddhartha Bhattacharyya and Paramartha Dutta, IGI Global, 2013, pp. 200-227. https://doi.org/10.4018/978-1-4666-2518-1.ch008

APA

Saha, S., Basu, S., & Nasipuri, M. (2013). Development of a Stop-Line Violation Detection System for Indian Vehicles. In S. Bhattacharyya & P. Dutta (Eds.), Handbook of Research on Computational Intelligence for Engineering, Science, and Business (pp. 200-227). IGI Global. https://doi.org/10.4018/978-1-4666-2518-1.ch008

Chicago

Saha, Satadal, Subhadip Basu, and Mita Nasipuri. "Development of a Stop-Line Violation Detection System for Indian Vehicles." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, edited by Siddhartha Bhattacharyya and Paramartha Dutta, 200-227. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2518-1.ch008

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Abstract

In the present work, the authors designed and developed a complete system for generating the list of all violating vehicles that has violated the stop-line at a road crossing automatically from video snapshots of road-side surveillance cameras using background subtraction technique. It then localizes the license plates of the vehicles by analyzing the vertical edge map of the images, segments the license plate characters using connected component labeling algorithm, and recognizes the characters using back propagation neural network. Considering round-the-clock operations in a real-life test environment, the developed system could successfully track 92% images of vehicles with violations on the stop-line in a red traffic signal. The performance of the system is evaluated with a dataset of 4717 images collected from 13 different camera views in 4 different environmental conditions. The authors have achieved around 92% plate localization accuracy over different views and weather conditions. The average plate level recognition accuracy of 92.75% and character level recognition accuracy of 98.76% are achieved over the localized vehicle images.

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