# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 1 Introduction  
The past decades have been witnessing a global-wise urbanization at an unprecedented speed. According to the World Urbanization Prospects by the United Nations Department of Economic and Social Affairs (UN DESA) Population Division, by 2014, 54% of the population (3.9 billion) in the world lived in urban areas, and this percentage is visioned to achieve 66% by 2050 [1]. While a larger population, more attractive services, and advanced infrastructures bring prosperity, urbanization also incurs many new challenges to city administrators and urban planners. It is non-trivial to provide a satisfactory living quality and maintain the sustainability of smart cities. Real-time information collection and decision-making are essential for a good understanding of dynamic city elements and timely reactions to emergencies [2, 3]. Pervasively 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]. Situational awareness (SAW) is among the most crucial issues in smart cities [5, 6]. In terms of safety, security, and sustainability of smart cities, urban surveillance services, such as object-of-interest identification [7] and anomalous behaviorrecognition [8], require not only high accuracy performance, but also real-time data-driven decision-making to avoid unacceptable consequences resulting from insufficient data assessment or incorrect event prediction [3]. However, it is a very challenging task to process an overwhelmingly large amount of urban surveillance data in a timely manner, which is dynamic, heterogeneous, and very diverse. Among the top concerned topics in smart cities, urban traffic surveillance is an indispensable component. Road safety is an important target in the global Sustainable Development Goals (SDGs). By 2030, the goal is to provide a safe, affordable, accessible and sustainable transport system for all residents [9]. Abnormal driving behaviors have been a challenge for decades since it often seriously jeopardizes the efficiency of traffic, endangers road safety, and even results in public emergencies. Therefore, it is critical to detect and interpret abnormal driving behaviors as early as possible. With the proliferation and ubiquitous deployment of IoTs, traditional solutions like human monitoring on site are being converted into many more automatic and intelligent ways. On the one hand, traffic data can be collected in real time and transferred to cloud centers where machine learning (ML) algorithms will be applied to recognize abnormal traffic patterns. On the other hand, nowadays, with ubiquitously presented smart devices, edge computing [10] rises as a new distributed computing paradigm at the network edge allowing onsite artificial intelligence much closer to the source data for real time decision-making [11; 12; 13]. Being considered as an extension of cloud computing, edge computing has shown many advantages and is promising to meet the requirements from smart city surveillance applications [14]. Using ubiquitously deployed computing, communication and storage at the edge, edge computing provides on-site/near-site storage and real-time task-processing capabilities, particularly for latency-sensitive applications [15]. Currently, regarding edge computing, there is a close concept named fog computing. For the sake of clarity in this paper, though fog computing emphasizes more on fog servers (like cloudlets or powerful laptops), while edge computing is closer to the network edge where computing nodes could be sensors or actuators; these two concepts will be treated the same, and the term edge computing will be used mostly in the rest of this paper. Because of the superior performance, Deep Neural Networks (DNNs) are widely adopted in pattern recognition applications [16]. To accommodate edge computing, there are many research efforts attempting to deconstruct DNNs and partitioned networks are allocated on different layers through an edge-fog-cloud computational architecture [17].