Title: Real time people flow counter and estimation using deep learning

Authors: R. Anand; P.K. Kalkeseetharaman; N. Naveen Kumar; Beegala Sasipreetham

Addresses: Department of Electronics and Communication Engineering, Sona College of Technology, Salem, India ' Department of Electronics and Communication Engineering, Sona College of Technology, Salem, India ' Department of Electronics and Communication Engineering, Sona College of Technology, Salem, India ' Department of Electronics and Communication Engineering, Sona College of Technology, Salem, India

Abstract: A real-time people flow counter and estimation using deep learning is the significant device in computer vision and a predictable knowledge discovery application in security, entertainment, tourism and corporate business. However, the state-of-the-art machine learning, deep learning and computer vision methods have complete this technology as a game-altering and even better human identification and counting part in terms of accurateness. This paper focuses on one of the progressive deep learning tools in people counting to achieve higher efficiency. Also, focus on image processing approach to count the number of people entering and exiting a defined place using neural networks. Here, the model is trained by artificial neural networks and computer vision. Real-time people flow estimation is very important for several applications like security, business, tourism, and other fields where people flow surveillance is required.

Keywords: centroid tracker; human detection; artificial intelligence; deep learning; OpenCV; neural network.

DOI: 10.1504/IJISC.2021.119076

International Journal of Intelligence and Sustainable Computing, 2021 Vol.1 No.3, pp.203 - 211

Received: 17 Apr 2020
Accepted: 17 Jun 2020
Published online: 22 Nov 2021 *

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