Abstract
Automated detection of vehicle lights can be used as a part of the systems for forward collision avoidance and accidents. This paper presents automatic vehicle detection and tracking system using Haar-Cascade method. The threshold collection is HSV color space in the vehicle light representation and the segmentation selection of light area. The extracted vehicle brake and turn indicator signals are morphologically paired and vehicle light candidate information is extracted by identifying the Region of Interest (ROI). The canny edge light intensity of the vehicle is extracted and a novel feature is called Edge Block Intensity Vector (EBIV). In this experiment, traffic surveillance system is developed for recognition of moving vehicle lights in traffic scenes using SVM with polynomial and RBF (Radial Basis Function) kernel. The experiments are carried out on the real time data collection in traffic road environment. This approach gives an overall average higher accuracy 95.7 % of SVM with RBF kernel by using 36 EBIV features compared to SVM with Polynomial kernel by using 9, 16, 25, 36, 64 and 100 EBIV features and SVM with RBF kernel by using 9, 16, 25, 64 and 100 EBIV features for recognizing the vehicle light.
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Sathya, R., Geetha, M.K., Aboorvapriya, P. (2015). Vehicle Brake and Indicator Detection for Autonomous Vehicles. In: Abawajy, J., Mukherjea, S., Thampi, S., Ruiz-Martínez, A. (eds) Security in Computing and Communications. SSCC 2015. Communications in Computer and Information Science, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-22915-7_36
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DOI: https://doi.org/10.1007/978-3-319-22915-7_36
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