IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems

Security and the smart city: A systematic review

The implementation of smart technology in cities is often hailed as the solution to many urban challenges such as transportation, waste management, and environmental protection. Issues of security and crime prevention, however, are in many cases neglected. Moreover, when researchers do introduce new smart security technologies, they rarely discuss their implementation or question how new smart city security might affect traditional policing and urban planning processes. This systematic review explores the recent literature concerned with new ‘smart city’ security technologies and aims to investigate to what extent these new interventions correspond with traditional functions of security interventions. Through an extensive literature search we compiled a list of security interventions for smart cities and suggest several changes to the conceptual status quo in the field. Ultimately, we propose three clear categories to categorise security interventions in smart cities: Those interventions that use new sensors but traditional actuators, those that seek to make old systems smart, and those that introduce entirely new functions. These themes are then discussed in detail and the importance of each group of interventions for the overall field of urban security and governance is assessed

A review of the internet of floods : near real-time detection of a flood event and its impact

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future

Big data analytics and processing for urban surveillance systems

Urban surveillance systems will be more demanding in the future towards smart city to improve the intelligence of cities. Big data analytics and processing for urban surveillance systems become increasingly important research areas because of infinite generation of massive data volumes all over the world. This thesis focused on solving several challenging big data issues in urban surveillance systems. First, we proposed several simple yet efficient video data recoding algorithms to be used in urban surveillance systems. The key idea is to record the important video frames when cutting the number of unimportant video frames. Second, since the DCT based JPEG standard encounters problems such as block artifacts, we proposed a very simple but effective method which results in better quality than widely used filters while consuming much less computer CPU resources. Third, we designed a novel filter to detect either the vehicle license plates or the vehicles from the images captured by the digital camera imaging sensors. We are the first to design this kind of filter to detect the vehicle/license plate objects. Fourth, we proposed novel grate filter to identify whether there are objects in these images captured by the cameras. In this way the background images can be updated from time to time when no object is detected. Finally, we combined image hash with our novel density scan method to solve the problem of retrieving similar duplicate images

Performance of video processing at the edge for crowd-monitoring applications

Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc. Using a cloud-centric approach where data is funneled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues. With the development of edge computing, a new paradigm for smart data management is emerging. Raw video feeds can be pre-processed at the point of capture while integration and deeper analytics is performed in the cloud. In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on low-powered gateway devices. We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and re-scaling video frames can optimise processing and performance. Increased edge processing resolves a number of issues in video analytics for crowd monitoring applications