Abstract
Previous systems used location information like GPS and the Suns location to detect sun light. However how much sunshine an area gets depends on its surround environment too, for instance we seldom get sunshine under a big tree or near a big building. So, we propose estimating sunshine hour just with a video by using image processing. We also calculate sunlight moving direction. One day outdoor video such as backyard, park or forest is processed to measure sunshine hour for every pixel to determine location of sunniest area. Shadow detection based on an algorithm using LAB color space where a difference in the light channel L is compared to neighbours to determine shadow. We improved this common algorithm by using adaptive threshold based on histogram of each frame of the video to overcome difficulty in tree and leaves shadow detection during sunset scene. We have tested 8 videos and the shadow detection rate has been improved to 93.04 from 85.34 by previously published algorithm. Then we use resultant image showing amount of sunlight on each pixel to obtain the sunshine hours. In addition, we calculate a sun direction from these images by using tracking algorithm for shadow movement.
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Light Trac. http://www.lighttracapp.com/
Sun Surveyor. http://www.sunsurveyor.com/
Sun Seeker. http://www.ozpda.com
SunCalc. http://suncalc.net/
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Bansal, P., Sun, C., Lee, WS. (2017). Sunshine Hours and Sunlight Direction Using Shadow Detection in a Video. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_26
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DOI: https://doi.org/10.1007/978-3-319-59876-5_26
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