Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Residual TrafficNet

Despite the huge amount of traffic surveillance videos and images have been accumulated in the daily monitoring, deep learning approaches have been underutilized in the application of traffic intelligent management and control. In this paper, traffic images, including various illumination, weather conditions, and vast scenarios, are extracted from the current surveillance system using in Shaanxi Province and preprocessed to set up a proper training dataset. In order to detect traffic congestion, a network structure is proposed based on residual learning to be pre-trained and fine-tuned. The network is then transferred to the traffic application and re-trained with self-established training dataset to generate the TrafficNet. The accuracy of TrafficNet to classify congested and uncongested road states reaches 99% for the validation dataset and 95% for the testing dataset. The proposed TrafficNet are verified by a regional detection of traffic congestion on a large-scale surveillance system currently using in China. The effectiveness and efficiencies are magnificently demonstrated with quick detection in the high accuracy in the case study. The experimental trial could extend its successful application to traffic surveillance system and has potential enhancement for intelligent transport system in future.