Abstract
The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme.
Highlights
The past decades have been witnessing a global-wise urbanization at an unprecedented speed
Deployed smart sensors and devices led us into the era of Internet of Things (IoT), which provides a solid foundation for instant decision-making [4]
A novel framework is proposed to detect and recognize abnormal vehicle behaviors by leveraging the multidimensional Singular Spectrum Analysis (mSSA) algorithm and Capsules Networks at the edge; A new cascaded Capsules Network structure is introduced with a new routing agreement for abnormal vehicle behavior recognition; and Extensive experimental studies have been conducted with real-world traffic data that validated the effectiveness of Smart Urban traffic Monitoring (SurMon) scheme
Summary
The past decades have been witnessing a global-wise urbanization at an unprecedented speed. All urban data need to be transferred to edge/fog layers or the cloud layer for inference This could explode network traffic, and, even in extreme situations, there could be no connection at all. A Smart Urban traffic Monitoring (SurMon) scheme is proposed following an edge computing paradigm. A novel framework is proposed to detect and recognize abnormal vehicle behaviors by leveraging the mSSA algorithm and Capsules Networks at the edge;. A new cascaded Capsules Network structure is introduced with a new routing agreement for abnormal vehicle behavior recognition; and Extensive experimental studies have been conducted with real-world traffic data that validated the effectiveness of SurMon scheme.
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