Abstract
Traffic detection and interpretation of its correct state is one of the most important information for developing an autonomous vehicle navigation system. Traffic light detection helps the autonomous vehicle to navigate safely in outdoor environment. In this paper, a vision-based algorithm is developed for traffic light detection and recognition. A monocular camera is used for capturing the surrounding outdoor environment. Intensity features are extracted from the templates of the traffic light and, the detection system is trained with these features. Similar features are searched in a predefine region of interest in the acquired image. Highly match candidates are considered as suitable traffic light candidate. The proposed algorithm is implemented in Labview NI-VISION system. Labview acquires data very effectively and effectively performs real-time processing. Using NI-VISION platform based system can go from design to test with minimum system changes. Adaptation of changing requirement is easy and less time consuming for Labview-based system. The algorithm is tested on different light condition images to check the reliability of system. Results show that developed algorithm is highly effective in real-time application for autonomous vehicle.
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Alam, A., Jaffery, Z.A. (2019). A Vision-Based System for Traffic Light Detection. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_32
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DOI: https://doi.org/10.1007/978-981-13-1819-1_32
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