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Keywords = Placemeter

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13 pages, 7010 KB  
Article
An Automatic Car Counting System Using OverFeat Framework
by Debojit Biswas, Hongbo Su, Chengyi Wang, Jason Blankenship and Aleksandar Stevanovic
Sensors 2017, 17(7), 1535; https://doi.org/10.3390/s17071535 - 30 Jun 2017
Cited by 37 | Viewed by 11718
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. [...] Read more.
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. Full article
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