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People Counting System

Ido Galil and Or Farfara

Supervised by Yaron Honen


This paper presents the development and details for use of a people’s counting system, able to count the people entering and exiting a specific zone from a live stream video feed obtained from IP cameras in real-time.
This goal is achieved by the combination of two components: a detector component using a Convolutional Neural Net (YOLO) detecting people on the frame, and a tracking component utilizing a tracking algorithm (CSRT) which updates those people positions on the next frames.
This system was built for the Technion’s libraries to monitor the amount of people in the libraries at any given time but is highly configurable and can fit different types of building and entrances.

Project People Counting System Picture 1
Final Presentation

Please, see final presentation.

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