Counting People in Crowded Places using Convolutional Neural Networks
Journal Title: Revista Romana de Interactiune Om-Calculator - Year 2019, Vol 12, Issue 2
Abstract
Security cameras have been around for decades and they continue to grow in number and capabilities as time passes. These days, in addition to the security nature of these cameras, they can be also used to obtain relevant information about the scene. Counting people and classifying them by gender, age, weight, height, or other attributes are very useful for marketing purposes and much more. Detecting anomalous events, like a suspicious luggage left unattended in a crowd or a person falling on subway rails could improve security in many spaces that are frequented by large masses of people. Extracting such data from video streams can have many more uses, some of which are not even imagined yet. In this paper we present how object recognition, tracking and classification methods can be used in conjunction to offer a solution for the problems described above.
Authors and Affiliations
Eduard Cojocea, Traian Rebedea
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