This chapter introduces the background of intelligent video surveillance system, cloud computing paradigm and the Hadoop framework. In addition, the Hadoop2- YARN is discussed to show the improvements made in Hadoop2 over Hadoop1. In this thesis, research is limited to the Hadoop1 MapReduce processing engine in order to avoid version compatibility issues that are often found with the software and tools used to construct distributed video analytics. Moreover the related work were described to identify the gaps in the research field.
Having completed the above study of the research background and related work, in the following chapters the thesis aims to contribute with novel knowledge that will further extend the state-of-art in the area of video surveillance system deployment, analysis, modelling and optimisation.
Video in Cloud Computing: The
Challenges & Recommendations
In this chapter, we review the legal implications of deploying large scale video surveillance in a public cloud and determine the practicalities and challenges that need to be met to abide by the law. The research findings of this chapter provide recommendations for the design of a large-scale cloud-based video forensic sys- tem. The chapter brings together legal, policy related and technical requirements pertaining to the design, installation, commissiond and operation of large scale video surveillance in a public environment bridging an existing gap in academic and industry research.
3.1
Introduction
Present video surveillance systems that typically consist of a large number of dis- tributed and networked CCTV cameras, collect significant quantities of digital evidence that can be used for crime forensics. The evolution of such systems have at present resulted in a significant proportion of the labour intensive video ana- lytic and forensic tasks, usually carried out by trained CCTV operators, to be alternatively carried out by intelligent, automated, computer based analysis sys- tems. Such systems use image processing, computer vision, pattern recognition and machine learning algorithms to detect and recognize objects of interest (e.g., people, vehicles etc.) and identify events of significance (e.g., person running, car speeding, people fighting etc.) enabling real-time alerts/warnings (i.e. video ana- lytics) to be generated or objects/events to be indexed and stored in a database to allow on-line search to be carried out (e.g. search for a man wearing a red shirt who entered a specific named building between 1pm to 3pm during a given week) for video forensic investigations (i.e. post incident analysis). However conducting
efficient video forensics analysis on large datasets of video by distributed camera systems require high performance computing capabilities due to the complexities of computing algorithms to be utilized and the significant storage capacity re- quired due to the sheer volume of data usually recorded. These two requirements increase the burden on the IT infrastructure to be used and introduce import- ant challenges that need to be met to ensure practical viability of systems. In response to meeting the above challenges at present there are initiatives to move video analytics/forensics, typically carried out using dedicated storage and com- puting infrastructure to the cloud to best utilize its potential benefits in providing on-demand resource pooling (both compute power and storage). Although cloud computing and related infrastructure can support the above mentioned critical requirements of modern intelligent, automated video surveillance systems it also introduces other technical and non-technical challenges. Security and privacy risks are the most cited challenges in the area of cloud computing[75] due to the custom- ers/users lack of physical control and the multi-tenancy nature of the cloud. Yet this is of fundamental importance in video evidence analytics and forensics, given the potential legal use of the evidence stored and/or created. Since video evidence gathering and use is regulated by law, it is crucial to review the legal implications of deploying video surveillance in the cloud and determine the practicalities and challenges that need to be met to abide by the law.
According to the research conducted within the remits of the research presen- ted in this thesis there has not been any previous attempt in studying the legal requirements of a video forensic system and investigating the viability of develop- ing a cloud based computing system for video forensics, given the known security and privacy threats of cloud computing.
While allocation and provisioning of virtual and physical sources in cloud are outside the control of cloud user, users need to specify the type and number of virtual machines that meets their application performance goal. This is a challenge since creating many virtual machines may lead to underutilized resources and may not also be cost-effective since in a public cloud the processing time is charged in an hourly basise[e.g Amazon EC2]. Furthermore, if less machines are created, it may affect performance expectation. This resource provisioning issue is an open research problem in cloud computing infrastructure management. This aim is to optimize the underlying resource utilization with a trade-off between resource cost and performance to meet a given customer’s service level agreement(SLA) within a given budget.
This chapter attempts to bridge these research gaps and make relevant recom- mendations for the design of a large-scale, cloud-based video forensic systems.