Intelligent Video Ingestion for Real-time Traffic Monitoring

<jats:p> As an indispensable part of modern critical infrastructures, cameras deployed at strategic places and prime junctions in an intelligent transportation system can help operators in observing traffic flow, identifying any emergency situation, or making decisions regarding road congestion without arriving on the scene. However, these cameras are usually equipped with heterogeneous and turbulent networks, making the real-time smooth playback of traffic monitoring videos with high quality a grand challenge. In this article, we propose a lightweight Deep Reinforcement Learning-based approach, namely, <jats:italic>sRC-C</jats:italic> <jats:styled-content style="black">(smart bitRate Control with a Continuous action space)</jats:styled-content> , to enhance the quality of real-time traffic monitoring by adjusting the video bitrate adaptively. Distinguished from the existing bitrate adjusting approaches, <jats:italic>sRC-C</jats:italic> can overcome the bias incurred by deterministic discretization of candidate bitrates by adjusting the video bitrate with more fine-grained control from a continuous action space, thus significantly improving the Quality-of-Service (QoS). With carefully designed state space and neural network model, <jats:italic>sRC-C</jats:italic> can be implemented on cameras with scarce resources to support real-time live video streaming with low inference time. Extensive experiments show that <jats:italic>sRC-C</jats:italic> can reduce the frame loss counts and hold time by 24% and 15.5%, respectively, even with comparable bandwidth utilization. Meanwhile, compared to the-state-of-art approaches, <jats:italic>sRC-C</jats:italic> can improve the QoS by 30.4%. </jats:p>