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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 peopleâ€™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 Technionâ€™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.
Please, see final presentation.