Abstract
In this paper, we present an IoT-based solution that can reduce the complexity of crowd estimation. About the human crowd estimation many techniques are in existence but nowadays more work is going on in this field because this is era of IoT and most of the organization is shifted toward IoT-based system. So in our proposed system we are using the Raspberry Pi-3 which are having quad-core processor that can be very useful and gives better result and accurate number even when the humans are very close to each other. This IoT-based model can easily be implemented in crowded areas and monitor the same. The camera module in this model also helps to differentiate between human and other bodies. As this is a mobile model, it can be easily fixed on the walls of street light and in the time of darkness or in night the camera captures clear images for process in the presence of street light. So that this model gives better result almost 70% better result in compare to exiting approaches.
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Kumar, A., Kumari, M. (2020). Design and Analysis of IoT-Based System for Crowd Density Estimation Techniques. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_29
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DOI: https://doi.org/10.1007/978-981-15-0694-9_29
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