# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 3 SurMon Architecture Overview  
Figure 1 presents the overall architecture of the proposed Smart Urban traffic Monitoring (SurMon) system, which includes three layers: end layer, edge layer and cloud layer. End layer consists of smart sensors and devices that collect urban traffic data. In the prototype of the SurMon system, drones are adopted to collect videos over a certain road area in real time, and the h264 coded video streams are transmitted to the ground controller that functions as the edge layer, where compressed video streams get decoded and displayed to the human operator. While the end layer is in charge of data collection and light-weight data pre-processing tasks, the edge layer operates near-site and performs data analysis for real-time decision  
Figure 1: Overview of the SurMon framework. making. Edge computing leverages heterogeneous smart devices as edge nodes. They collaborate with each other to facilitate surveillance tasks with a lower latency. The smart devices include smart phones, tablets, personal laptops, etc. In the SurMon system, major computations take place at the edge layer including vehicle detection, vehicle tracking, anomalous behavior detection, and interpretation. Vehicle detection and tracking tasks extract vehicle trajectories in real time. Due to the resources constraint on smart devices and the complexity of tracking tasks, it entails the collaboration among a number of edge nodes, which are divided into master and slave nodes [69]. The master node orchestrates the collaboration among slave nodes. In the SurMon prototype system, for instance, the in-car user laptop plays the role of the master edge node. It receives video streams from drones and then dispatches decoded video frames to each slave node. Each edge node tracks one single vehicle and transmits the vehicle trajectory data back to the master node for other tasks. Therefore, the computing intensive multi-target tracking task is reformatted as multiple single-target tracking tasks [69]. A task assignment scheme is designed to ensure the synchronization among edge nodes. The video streams from drones are transferred to the ground controller. Once suspicious vehicles are identified, each individual vehicle and the region-of-interest are assigned to a target tracker for consistent tracking and velocity estimation. When more suspicious vehicles are detected, the controller checks with the edge nodes around and dispatches a single target tracking task to one who has sufficient computing power to process the job. Readers who are interested in the details of the rationale and implementation of multiple targets tracking at the edge based on a single target tracking algorithm are referred to [69]. Besides task assignments to each slave node, master node consolidates all trajectories data from each edge node and attempt to detect abnormal driving behaviors and interpret them to decide whether it is appropriate or not. Instead of trying to pre-define driving patterns of abnormal behaviors, SurMon proposes to focus on behaviors that are _different_ from others. Multi-dimensional Singular Spectrum Analysis (mSSA) is adopted to reformulate this problem as a change point detection problem [22]. On the one hand, defining abnormal patterns in advance results in detection inaccuracy since, in practice, it is very difficult to enumerate every abnormal pattern; on the other hand, behaviors that bias away from average patterns are identified first, which effectively reduces computations on edge nodes and the amount of data that is transmitted to a remote cloud center if necessary. However, one trade-off of this approach is that, after the detection of different behaviors, it cannot be simply categorized as aggressive or abnormal driving behaviors in certain situations. For example, in the United States, one of the right-hand drive countries, if a vehicle stops waiting for a left turn while all others are moving forward, this behavior is likely detected and marked as a difference, but it is actually not a violation of traffic rules.