V-ITS: Video-based Intelligent Transportation System for Monitoring Vehicle Illegal Activities

Abstract

Vehicle monitoring is a challenging task for videobased intelligent transportation system (V-ITS). Nowadays, the V-ITS system has a significant socioeconomic impact on the development of smart cities and always demand to monitor different traffic parameters. It noticed that traffic accidents are exceeded throughout the world with the percentage of 1.7%. The increase in accidents and the percentage of deaths are due to the people that don't abide by the traffic rules. To address these challenges, an improved V-ITS system is developed in this paper to detect and track vehicles and driver's activities during highway driving. This improved V-ITS system is capable to do automatic traffic management that saves traffic accidents. It provides the feature of a real-time detection algorithm for driver immediate line overrun, speed limit overrun and yellow-line driving. To develop this V-ITS system, a pre-trained convolutional neural network (CNN) model with 4-layer architecture was developed and then deep-belief network (DBN) model was utilized to recognize illegal activities. To implement V-ITS system, OpenCV and python tools are mainly utilized. The GRAM-RTM online free data sets were used to test the performance of V-ITS system. The overall significance of this intelligent V-ITS system is comparable to other state-of-the-art systems. The real-time experimental results indicate that the V-ITS system can be used to reduce the number of accidents and ensure the safety of passengers as well as pedestrians.