Convolutional neural network for recognizing highway traffic congestion

Abstract We investigates the performance of deep Convolutional Neural Network (CNN) for recognizing highway traffic congestion state in surveillance camera images. Different from the usual images in ImageNet, images generated by highway surveillance 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 variance of scenes, road configurations. We are very interested to study whether convolutional 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 recognition accuracy of 98% on held-out test samples. And many of the misclassified images turn out to be borderline cases. More results include that scale and perspective in photography could affect the recognition result.