Abstract
Video surveillance has been in use since a protracted time as an assistance to beat security and other problems. Historically, the video outputs area unit monitored by human operators and area unit sometimes saved to tapes for later use. Sensitive areas like shopping malls, banks, huddled public places want a strict police investigation and may require management of the flow of individuals mechanically. To do such automation, a wise video closed-circuit television is required for today’s world equipped with machine learning algorithms. In this project, a sensible visual closed-circuit television with person detection and following capabilities is bestowed. This can be used to regulate the flow of persons into the sensitive areas, which is often achieved by count the persons who are getting into and going through these areas, so knowing the overall capability a sensitive space is holing at any specific purpose of your time. Motion vector analysis is that the main construct that is used here to realize the following of the persons. This has a tendency to count the persons who are getting into and going out stationary cameras fixed points, the capability is obtained as distinction between the count of the persons entered and count of the persons who left the sensitive space. Any sensitive space would have a restricted house to accommodate. So it is necessary to prohibit the persons from getting into sensitive space, once the capability is reached to threshold price.
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Acknowledgements
The authors express deep sense of gratitude to the management of DIET College and GITAM University for the entire support and facilities provided to us throughout in bringing out this successful work.
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Sujatha, K., Rama Raju, S.V.S.V.P., Nageswara Rao, P.V., Arjuna Rao, A., Shyamanth, K. (2019). Person Tracking and Counting System Using Motion Vector Analysis for Crowd Steering. In: Chaki, N., Devarakonda, N., Sarkar, A., Debnath, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 28. Springer, Singapore. https://doi.org/10.1007/978-981-13-6459-4_25
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DOI: https://doi.org/10.1007/978-981-13-6459-4_25
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