Cycling is an integral part of daily life for many people. This project presents a vision-based cyclist travel lane and helmet detection system. This system can serve as surveillance to detect whether the cyclist is traveling in a devoted lane and wearing a helmet for safety measures. The model involves the application of scale-invariant feature extraction (SIFT) algorithm for feature description. The detection method is based on six machine-learning classification algorithms. The classifiers are evaluated based on testing accuracy, and the best classifier is selected for the final model creation. The random forest classifier provided highest training accuracy of 99% and testing accuracy of 85.44% for cyclist travel lane detection. The same classifier provided the highest training accuracy of 99.53% and testing accuracy of 87.83% for cyclists’ helmet detection. In future, this system can also serve as a small part of autonomous driver assistance systems by detecting the right lane.
Vision-Based Cyclist Travel Lane and Helmet Detection
Lect. Notes in Networks, Syst.
International Conference on Machine Learning, IoT and Big Data ; 2023 ; Sarang, India March 10, 2023 - March 12, 2023
06.10.2023
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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