Abstract
This paper aims to tackle the problem of impaired visibility for drivers on the road due to fog, which is a safety concern. This novel approach is a unique comparative algorithm through integration with Google Maps and has several embedded functionalities to reduce noise caused by fog. Real-time input is collected in the form of continuous video frames, on which image processing is carried out. This is a two-step process, first using dark channel prior and second using histogram matching with ideal weather Google Street View images. In order to measure the fogginess of the image at each step, horizontal variance is used. The results obtained show a drastic increase in variance during the two-step process, which is in line with the theory that the higher the variance, the lesser the fogginess. The fog-free images are retrieved and put together to form continuous frames of a video, which is displayed on the driver’s screen in real time.
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Acknowledgements
The authors are greatly appreciative for the support provided by Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, UAE. Further, the lab facilities provided a conducive environment for innovation.
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DSouza, A.S., Mohamed, R., Mishika, Kalaichelvi, V. (2020). Real-Time Fog Removal Using Google Maps Aided Computer Vision Techniques. In: Goel, N., Hasan, S., Kalaichelvi, V. (eds) Modelling, Simulation and Intelligent Computing. MoSICom 2020. Lecture Notes in Electrical Engineering, vol 659. Springer, Singapore. https://doi.org/10.1007/978-981-15-4775-1_62
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DOI: https://doi.org/10.1007/978-981-15-4775-1_62
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