Abstract
Computer vision techniques are expected to significantly improve the development of intelligent transportation systems (ITS), which are anticipated to be a key component of future Smart City frameworks. Powered by computer vision techniques, the conversion of existing traffic cameras into connected “smart sensors” called intelligent video analysis (IVA) systems has shown the great capability of producing insightful data to support ITS applications. However, developing such IVA systems for large-scale, real-time application deserves further study, as the current research efforts are focused more on model effectiveness instead of model efficiency. Therefore, we have introduced a real-time, large-scale, cloud-enabled traffic video analysis framework using the NVIDIA DeepStream and NVIDIA Metropolis. In this study, we have evaluated the technical and economic feasibility of our proposed framework to help traffic agency to build IVA systems more efficiently. Our study shows that the daily operating cost for our proposed framework on Google Cloud Platform is less than $0.14 per camera, and that, compared with manual inspections, proposed framework achieves an average vehicle-counting accuracy of 83.7% on sunny days.
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