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Title |
Deep convolutional neural network architecture for urban traffic flow estimation |
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Author |
Sabbani imad, Perez-uribe Andres, Bouattane Omar, El Moudni Abdellah |
| Citation |
Vol. 18 No. 7 pp. 69-75 |
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Abstract |
Road traffic density estimation can be very helpful for the successful deployment of intelligent Transportation systems. In this paper, we introduce a deep convolutional neural network (DCNN) based method that learns traffic density from pre-labeled images in order to estimate the traffic flow density in highways. Our method classifies the traffic flow density into three different states: light, medium and heavy. A standard database of real videos from Seattle roads was used to develop our proposed approach. The cross-validation and the class activation mapping techniques were employed in this work, in order to evaluate the performance of our method. The results show that our model outperformed all the existing conventional methods by reaching the highest accuracy of 99,62%. |
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Keywords |
Pattern recognition, Video processing, Road traffic density, Deep convolutional neural network |
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