Abstract
Vehicle light detection and recognition for collision avoidance presents a major challenge in urban driving conditions. In this chapter, an optical flow method is used to extract moving vehicles in a traffic environment, and hue-saturation-value (HSV) color space is adopted to detect vehicle brake and turn light indicators. In addition, a morphological operation is applied to obtain the precise vehicle light region. The proposed Vehicle Light Block Intensity Vector (VLBIV) feature extraction from the vehicle light region is realized by a supervised learning method known as support vector machines (SVM). Analysis is carried out on the vehicle signal recognition system which interprets the color videos taken from a front-view video camera of a car operating in traffic scenarios. This technique yields average accuracy of 98.83% in SVM (RBF) in 36 VLBIV features when compared to an SVM (polynomial) classifier.
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Arunnehru, J., Anwar Basha, H., Kumar, A., Sathya, R., Kalaiselvi Geetha, M. (2019). A Vision-Based On-road Vehicle Light Detection System Using Support Vector Machines. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_13
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