Citation

Biswas D, Su H, Wang C, Blankenship J, Stevanovic A. Sensors (Basel) 2017; 17(7): s17071535.

Affiliation

Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL 33431, USA. astevano@fau.edu.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s17071535

PMID

28665360

Abstract

Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars. OverFeat Framework is a combination of Convolution Neural Network (CNN) and one machine learning classifier (like Support Vector Machines (SVM) or Logistic Regression). With this study, we showed another possible application area for the OverFeat Framework. The advantages and shortcomings of the Background Subtraction Method and OverFeat Framework were analyzed using six individual traffic videos with different perspectives, such as camera angles, weather conditions and time of the day. In addition, we compared the two algorithms above with manual counting and a commercial software called Placemeter. The OverFeat Framework showed significant potential in the field of car counting with the average accuracy of 96.55% in our experiment.

Language: en

Keywords

Background Subtraction Method; Convolution Neural Network; OverFeat Framework; Placemeter; car counting