Abstract
AbstractAutomated 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.KeywordsSupport vector machineDriver assistance systemVehicle detection and vehicle light recognition
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.