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.
Highlights
In today’s world, properly maintaining the traffic system is a very tedious job
The performance measure of Background Subtraction Method (BSM) and OverFeat Framework has been evaluated with manual counting and Placemeter
OverFeat Framework is a combination of Convolution Neural Network (CNN) and another machine learning classifier (logistic regression (LR), support vector machines (SVM), etc.)
Summary
In today’s world, properly maintaining the traffic system is a very tedious job. Every day, the number of vehicles increases at exponential order [1]. Automatic vehicle counting is a key technique to monitor and estimate traffic flow. Car counting is important and helpful to optimize the traffic signaling system [2,3]. It helps to redirect the traffic to alternate less congested roads on a demanding day with special events. Understanding the physical traffic load is an important application of car counting. This enables an opportunity for transportation engineers and decision makers to plan their budget well before renovating an existing road or building a new road depending on the car density statistics. Our purpose in this study is to develop an automated car counting system from traffic videos that can perform well in both day and night, in sunny and cloudy weather conditions. We took into account the vibration effect caused by cameras being installed on a bridge or similar conditions
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