Truck Speed Detection Through Video Streams

Accurately assessing the speed of vehicles is important for traffic management systems. This is especially the case for heavy goods vehicles such as lorries/trucks, since they cannot easily stop at short notice. Previous work has shown that deep learning can be used for identifying and distinguishing trucks on the road from other vehicles, e.g., [1], however accurately estimating their speed from roadside cameras remains a challenge. One solution we employ is using video data from the roadside cameras, then extracting the speeds of vehicles in the video from the Infra-Red Traffic Logger (TIRTL) systems, which are provided by the Department of Transport, Victoria. The TIRTL system is very accurate but expensive and only deployed at a few key locations around Melbourne. A solution that works at the edge and uses lightweight Internet-of-Things devices to produce accurate speed data is thus highly desirable. In this paper, we propose a Convolutional Neural Network (CNN) model using a light-weight Siamese backbone and associated feature correlations to track and detect the speed of trucks. We build a dataset that contains images with speed and bounding-box annotations to train the proposed model. To enable the model to maintain a high degree of accuracy with different camera setups, we train and test the proposed model using image augmentation. The results show our model has an average speed estimation error of 4.92% and an average Intersection over Union (IoU) of 75.8% whilst incorporating different intrinsic and extrinsic parameters based on image augmentation. Such a capability has the potential to change the way services are deployed across the road network to record vehicle types and speeds.