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An Efficient Solution For People Detection, Tracking And Counting Using Convolutional Neural Networks

Author

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  • Eduard Cojocea

    (University Politehnica of Bucharest, Open Gov SRL, Bucharest, Romania)

  • Traian Rebedea

    (University Politehnica of Bucharest, Open Gov SRL, Bucharest, Romania)

Abstract

The number of unique persons walking near a shop or inside a mall is relevant since it can indicate the possible extension margin of a certain business. Also, being able to extract statistics regarding gender, age group and so on, can offer key insights regarding how to better manage and stock a business. In this paper we present a system which detects, tracks and counts the number of people in a video stream. The results obtained can be visualised in a GUI interface which allows for customizing multiple visualization tools. We use YOLOv3, a Convolutional Neural Network model, for object detection and Deep SORT for tracking. We describe how the system works on different hardware architectures: on a server with two high-end GPUs and on various edge devices, such as Raspberry Pi 3, Raspberry Pi 4 and NVidia Jetson TX2.

Suggested Citation

  • Eduard Cojocea & Traian Rebedea, 2020. "An Efficient Solution For People Detection, Tracking And Counting Using Convolutional Neural Networks," Journal of Information Systems & Operations Management, Romanian-American University, vol. 14(2), pages 49-56, December.
    • Handle: RePEc:rau:jisomg:v:14:y:2020:i:2:p:49-56
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