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http://dx.doi.org/10.5573/ieie.2017.54.3.70

A Real-time People Counting Algorithm Using Background Modeling and CNN  

Yang, HunJun (Dept. of Electronic Engineering, Inha University)
Jang, Hyeok (ETRI (Electronics and Telecommunications Research Institute))
Jeong, JaeHyup (Dept. of Electronic Engineering, Inha University)
Lee, Bowon (Dept. of Electronic Engineering, Inha University)
Jeong, DongSeok (Dept. of Electronic Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.3, 2017 , pp. 70-77 More about this Journal
Abstract
Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.
Keywords
Background Modeling; Object tracking; Convolutional Neural Network; People Counting;
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