Deep learning based smart traffic management using video analytics and IoT sensor fusion

Due to the rapid growth in population and continue rise in number of vehicle on road issue of transportation congestion arises. Combination of Internet-of-Things-Aided Smart Transportation System is doing promising work in this area. A massive amount of video streaming data is produced at high speed by distributed mobile IoT devices and video cameras due to the use of artificial intelligence (AI) and Internet of Things (IoT) combinations in smart city scenarios. Real-time data processing application demands efficient analysis of these data. The key focus in this work is on improving cloud-based traffic video analytics systems by executing a two-step approach: first, Edge-based pre-processing of a video stream to reduce data transmission time and Cloud-based traffic Video Analytics. Second, Video Analytics and Sensor Fusion (VA/SF) are studied and examined to guarantee that the continuum of potentials are sufficiently covered by the data that algorithms are trained on and make it sufficiently efficient to provide high accuracy or low latency modes of services. We suggest a YOLO based deep learning video analytics system on the cloud to perform real-time object detection for traffic surveillance video. The proposed VA/SF model reduces detection speed of the model while improving the object detection accuracy by 1.8% when compared to no-IoT sensor fusion. The experiment proves that higher accuracy with better detection is achieved by our traffic analytical model under extreme weather conditions.