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Convolutional neural network for recognizing highway traffic congestion
| 题名： | Convolutional neural network for recognizing highway traffic congestion |
| 正文语种： | 英文 |
| 作者： | Hua Cui;Gege Yuan;Ni Liu;Min gyuan Xu;Huansheng Song |
| 作者单位： | Chang'an University |
| 关键词： | AlexNet; Convolutional Neural Network (CNN); GoogLeNet; object classification; traffic congestion |
| 摘要： | We investigates the performance of deep Convolutional Neural Network (CNN) for recogniz-ing highway traffic congestion state in surveillance camera images. Different from the usual images in ImageNet, images gen erated by highway surveilla nee cameras usually have much more extensive range of perspective and thus larger area of background. Therefore the objective road and vehicles are not as prominent as target object in ImageNet images. And also these images from cameras across a large number of highway sites could show a very rich varianee of seenes, road configurations. We are very interested to study whether convo-lutional networks are still reliably able to classify such images, without any special previous processing such as segmentation of objective roads. Two classic convolutional networks, AlexNet and GoogLeNet are employed to classify congestion state. We build a highway imagery dataset using real-life traffic videos to evaluate the CNNs recognition performance. These images cover a wide range of road configurations, times of the day, weather and lighting conditions, and have been labeled with one of the two states, congestion or non-congestion. The experimental results indicate that under the current strategy of feeding images directly into networks, both AlexNet and GoogLeNet can achieve an excellent recog-nition accuracy of 98% on held-out test samples. And many of the misclassified images turn out to be borderli ne cases. More results in elude that scale and perspective in photography could affect the recog nition result. |
| 出版日期： | 2020 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | Vol24 |
| 期： | No01-06 |
| 页码： | 279-289 |
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