Abstract
In this work we developed a system for estimating the density levels in the holy mosque of Makkah using video cameras installed in the mosque. This set-up relies on dividing the image into smaller segments and counting the number of people in each segment to infer the density. This algorithm used texture and SIFT interest point features to get an accurate count of the number of people at each segment using support vector regression. Having segments at different sizes helped to account for objects with different size in the image. In addition, the use of overlapping segment smooth the estimated density maps as each pixel receive a contribution from different patches. Our methodology has been tested extensively with different cameras during the Fasting season of 2015 with images from very crowded areas in the mosque.
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Acknowledgements
This project has been funded by Transportation and Crowd Management Centre of Research Excellence (TCMCORE), in Umm Al-Qura University, Saudi Arabia under grant number (PR143508).
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Ali, Y.S., Zafar, B., Simsim, M. (2016). Estimation of Density Levels in the Holy Mosque from a Network of Cameras. In: Knoop, V., Daamen, W. (eds) Traffic and Granular Flow '15. Springer, Cham. https://doi.org/10.1007/978-3-319-33482-0_4
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DOI: https://doi.org/10.1007/978-3-319-33482-0_4
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