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This research evaluates crime prevention effects/impact of open-street closed circuit television (CCTV) surveillance systems as installed in the selected areas (research sites) of the cities of Johannesburg and Tshwane in the Gauteng Province of South Africa on crimes occurring in these surveilled areas. Currently, CCTV surveillance systems are a common sight in many of the urban areas of South Africa.The principal aim of this study was to explore the evaluation of CCTV for crime prevention, reduction and control. The results show that, despite a lack of empirical evidence as to the value of CCTV surveillance systems in preventing or reducing crime, there is strong public support for these systems and that the foundation for much of this support lies in the perceptions/feelings of members of the public of greater safety generated in areas with CCTV coverage. The method of sampling used was a purposive non-probability sampling approach. Participants were selected for interviews based on their knowledge and experience of CCTV systems. The results show that, despite this lack of empirical evidence, CCTV appears to be a viable option for crime prevention and control when integrated with evidence-based strategies rather than as a stand-alone tactic in order to achieve crime control benefits. / Criminology and Security Science / M. Tech. (Security Management)

Abstract Due to the development and proliferation of information and communication technologies, people¡¦s daily lives are indispensably relied on these tools. Based on concepts of safety, energy conservation, convenience, and comfort, intelligent buildings¡¦ designs have gradually become the main stream of the present and future architectures. Taiwan introduced the idea of the intelligent building since 1989. With the push of the automatic idea of the building, the security and surveillance systems integrators had become the new popular industry and the competition of the industry is more and more fierce. Understanding the key successful factors objectively is important of making administrative decision to security and surveillance systems integrator managers. In view of this, this research adopts the expert focus group interview and analytic hierarchy process theory. The author interview 10 high-level experts of the security and surveillance systems integrators and 10 customer experts of intelligence building traders to develop a set of the key success factors appraisal procedure to the security and surveillance systems integrators for intelligent buildings. Based on enterprise factor, staffs factor, and products factor, the author calculate the ranks and weights of the criterion and sub-criterion of the key successful factors. This can offer the security and surveillance systems integrator managers for intelligent buildings to make administrative decision. The study chooses three companies as an example to do empirical study, and compares their advantage and performance of the key successful factors. The result of study finds as follows. In criterion, the most important is produce factor, the less is staffs factor, and the end is enterprise factor. In sub-criterion, the most important is staff's professional ability technology, the less are price, products diversification, and customize degree. In ranks and weights, the last are productions separately, organized scale, and popularity and impressions. The conclusion can offer the security and surveillance systems integrator managers for intelligent buildings to make administrative decision.

Video surveillance is an active research area in Computer Vision and Machine Learning. It received a lot of attention in the last few decades. Maritime surveillance is the act of effective detection/recognition of all maritime activities that have impact on economy, security or the environment. The maritime environment is a dynamic environment. Factors such as constant moving of waves, sun reflection over the sea surface, rapid change in lightning due to the sun reflection over the water surface, movement of clouds and presence of moving objects such as airplanes or birds, makes the maritime environment very challenging. In this work, we propose a method for detecting a motion generated by a maritime vehicle and then identifying the type of this vehicle using classification methods. A new maritime video database was created and tested. Classifying the type of vehicles have been tested by comparing 13 image features, and two SVM solving algorithms. In motion detection part, multiple smoothing filters were tested in order to minimize the false positive rate generated by the water surface movement, the results have been compared to optical flow, a well known method for motion detection. / Master of Science

The Internet of Things (IoT) has transformed our technological landscape by seamlessly connecting devices, sensors, and systems, with surveillance systems being one of its prominent applications. These systems offer advanced monitoring, control, and analysis capabilities, enabling their deployment in diverse domains such as security, transportation, healthcare, and smart cities. However, ensuring the quality of IoT surveillance systems is crucial for their effective operation and performance. Despite their widespread adoption, there is a surprising lack of understanding and analysis of the quality characteristics associated with these systems, which poses challenges in managing and optimizing their performance and security. To address this knowledge gap, this thesis aims to provide a comprehensive review and analysis of the quality characteristics related to IoT surveillance systems, exploring the challenges and considerations in achieving these characteristics and discussing current research and practices in the field. The study reveals significant challenges in reliability, security, privacy, and usability, highlighting the need for measures such as redundancy, robust security protocols, privacy preservation techniques, and enhanced usability to optimize the performance and effectiveness of IoT surveillance systems. These findings will hopefully provide important insight for researchers, practitioners, and others involved in the development and implementation of IoT surveillance systems.

Among the diverse tasks performed by an intelligent distributed multi-camera surveillance system, person re-identification (re-id) is one of the most essential. Re-id refers to associating an individual or a group of people across non-overlapping cameras at different times and locations, and forms the foundation of a variety of applications ranging from security and forensic search to quotidian retail and health care. Though attracted rapidly increasing academic interests over the past decade, it still remains a non-trivial and unsolved problem for launching a practical reid system in real-world environments, due to the ambiguous and noisy feature of surveillance data and the potentially dramatic visual appearance changes caused by uncontrolled variations in human poses and divergent viewing conditions across distributed camera views. To mitigate such visual ambiguity and appearance variations, most existing re-id approaches rely on constructing fully supervised machine learning models with extensively labelled training datasets which is unscalable for practical applications in the real-world. Particularly, human annotators must exhaustively search over a vast quantity of offline collected data, manually label cross-view matched images of a large population between every possible camera pair. Nonetheless, having the prohibitively expensive human efforts dissipated, a trained re-id model is often not easily generalisable and transferable, due to the elastic and dynamic operating conditions of a surveillance system. With such motivations, this thesis proposes several scalable re-id approaches with significantly reduced human supervision, readily applied to practical applications. More specifically, this thesis has developed and investigated four new approaches for reducing human labelling effort in real-world re-id as follows: Chapter 3 The first approach is affinity mining from unlabelled data. Different from most existing supervised approaches, this work aims to model the discriminative information for reid without exploiting human annotations, but from the vast amount of unlabelled person image data, thus applicable to both semi-supervised and unsupervised re-id. It is non-trivial since the human annotated identity matching correspondence is often the key to discriminative re-id modelling. In this chapter, an alternative strategy is explored by specifically mining two types of affinity relationships among unlabelled data: (1) inter-view data affinity and (2) intra-view data affinity. In particular, with such affinity information encoded as constraints, a Regularised Kernel Subspace Learning model is developed to explicitly reduce inter-view appearance variations and meanwhile enhance intra-view appearance disparity for more discriminative re-id matching. Consequently, annotation costs can be immensely alleviated and a scalable re-id model is readily to be leveraged to plenty of unlabelled data which is inexpensive to collect. Chapter 4 The second approach is saliency discovery from unlabelled data. This chapter continues to investigate the problem of what can be learned in unlabelled images without identity labels annotated by human. Other than affinity mining as proposed by Chapter 3, a different solution is proposed. That is, to discover localised visual appearance saliency of person appearances. Intuitively, salient and atypical appearances of human are able to uniquely and representatively describe and identify an individual, whilst also often robust to view changes and detection variances. Motivated by this, an unsupervised Generative Topic Saliency model is proposed to jointly perform foreground extraction, saliency detection, as well as discriminative re-id matching. This approach completely avoids the exhaustive annotation effort for model training, and thus better scales to real-world applications. Moreover, its automatically discovered re-id saliency representations are shown to be semantically interpretable, suitable for generating useful visual analysis for deployable user-oriented software tools. Chapter 5 The third approach is incremental learning from actively labelled data. Since learning from unlabelled data alone yields less discriminative matching results, and in some cases there will be limited human labelling resources available for re-id modelling, this chapter thus investigate the problem of how to maximise a model's discriminative capability with minimised labelling efforts. The challenges are to (1) automatically select the most representative data from a vast number of noisy/ambiguous unlabelled data in order to maximise model discrimination capacity; and (2) incrementally update the model parameters to accelerate machine responses and reduce human waiting time. To that end, this thesis proposes a regression based re-id model, characterised by its very fast and efficient incremental model updates. Furthermore, an effective active data sampling algorithm with three novel joint exploration-exploitation criteria is designed, to make automatic data selection feasible with notably reduced human labelling costs. Such an approach ensures annotations to be spent only on very few data samples which are most critical to model's generalisation capability, instead of being exhausted by blindly labelling many noisy and redundant training samples. Chapter 6 The last technical area of this thesis is human-in-the-loop learning from relevance feedback. Whilst former chapters mainly investigate techniques to reduce human supervision for model training, this chapter motivates a novel research area to further minimise human efforts spent in the re-id deployment stage. In real-world applications where camera network and potential gallery size increases dramatically, even the state-of-the-art re-id models generate much inferior re-id performances and human involvements at deployment stage is inevitable. To minimise such human efforts and maximise re-id performance, this thesis explores an alternative approach to re-id by formulating a hybrid human-computer learning paradigm with humans in the model matching loop. Specifically, a Human Verification Incremental Learning model is formulated which does not require any pre-labelled training data, therefore scalable to new camera pairs; Moreover, the proposed model learns cumulatively from human feedback to provide an instant improvement to re-id ranking of each probe on-the-fly, thus scalable to large gallery sizes. It has been demonstrated that the proposed re-id model achieves significantly superior re-id results whilst only consumes much less human supervision effort. For facilitating a holistic understanding about this thesis, the main studies are summarised and framed into a graphical abstract.

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given Area Of Interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This thesis proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

Surveillance systems are usually employed to monitor wide areas in which their usersaim to detect and/or observe events or phenomena of their interest. The use ofwireless sensor networks in such systems is of particular interest as these networks can provide a relative low cost and robust solution to cover large areas. Emerging applications in this context are proposing the use of wireless sensor networks composed of both static and mobile sensor nodes. Motivation for this trend is toreduce deployment and operating costs, besides providing enhanced functionalities.The usage of both static and mobile sensor nodes can reduce the overall systemcosts, by making low-cost simple static sensors cooperate with more expensive andpowerful mobile ones. Mobile wireless sensor networks are also desired in somespecific scenarios in which mobility of sensor nodes is required, or there is a specificrestriction to the usage of static sensors, such as secrecy. Despite the motivation,systems that use different combinations of static and mobile sensor nodes are appearing and with them, challenges in their interoperation. This is specially the case for surveillance systems.This work focuses on the proposal of solutions for wireless sensor networks including static and mobile sensor nodes specifically regarding cooperative andcontext aware mission setup and performance. Orthogonally to the setup and performance problems and related cooperative and context aware solutions, the goalof this work is to keep the communication costs as low as possible in the executionof the proposed solutions. This concern comes from the fact that communication increases energy consumption, which is a particular issue for energy constrained sensor nodes often used in wireless sensor networks, especially if battery supplied. Inthe case of the mobile nodes, this energy constraint may not be valid, since their motion might need much more energy. For this type of node the problem incommunicating is related to the links’ instabilities and short time windows availableto receive and transmit data. Therefore, it is better to communicate as little as possible. For the interaction among static and mobile sensor nodes, all thesecommunication constraints have to be considered.For the interaction among static sensor nodes, the problems of dissemination and allocation of sensing missions are studied and a solution that explores local information is proposed and evaluated. This solution uses mobile software agentsthat have capabilities to take autonomous decisions about the mission dissemination and allocation using local context information so that the mission’s requirementscan be fulfilled. For mobile wireless sensor networks, the problem studied is how to perform the handover of missions among the nodes according to their movements.This problem assumes that each mission has to be done in a given area of interest. In addition, the nodes are assumed to move according to different movement patterns,passing through these areas. It is also assumed that they have no commitment in staying or moving to a specific area due to the mission that they are carrying. To handle this problem, a mobile agent approach is proposed in which the agents implement the sensing missions’ migration from node to node using geographical context information to decide about their migrations. For the networks combining static and mobile sensor nodes, the cooperation among them is approached by abiologically-inspired mechanism to deliver data from the static to the mobile nodes.The mechanism explores an analogy based on the behaviour of ants building and following trails to provide data delivery, inspired by the ant colony algorithm. It is used to request the displacement of mobile sensors to a given location according tothe need of more sophisticated sensing equipment/devices that they can provide, so that a mission can be accomplished.The proposed solutions are flexible, being able to be applied to different application domains, and less complex than many existing approaches. The simplicity of the solutions neither demands great computational efforts nor large amounts of memory space for data storage. Obtained experimental results provide evidence of the scalability of these proposed solutions, for example by evaluatingtheir cost in terms of communication, among other metrics of interest for eachsolution. These results are compared to those achieved by reference solutions (optimum and flooding-based), providing indications of the proposed solutions’ efficiency. These results are considered close to the optimum one and significantly better than the ones achieved by flooding-based solutions.

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given Area Of Interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This thesis proposes a market-based framework that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target-tracking are studied using the proposed framework as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively.

Video surveillance systems are a rapidly growing industry. As with most systems, this technology presents both opportunities and threats. The wide adoption of video surveillance systems by various businesses and individuals has raised some vital security issues.  Appropriately addressing these security issues is of great importance for video surveillance systems, as these systems may capture sensitive personal data and may attract numerous attacks. As of today nearly all devices have become networked (or are on their way to being connected to networks), hence eavesdropping is a common attack which can exploit a breach of a system’s security and result in data disclosure to unauthorised parties, video stream alterations, interference, and reduction of a system’s performance. Moreover, it is important that video surveillance systems are standardized by appropriate standardization organizations in order to assure high quality of the security services that utilize these systems and to facilitate interoperability. In this master thesis project rules and regulations concerning personal data protection were studied in order to define the requirements of the proposed robust and high quality security scheme that is to be integrated into video surveillance systems. This security scheme provides United States Federal Information (FIPS)* compliant security services by securing the communication channel between the system’s devices. The authentication of the system’s devices is established by using certificates and key exchanges. The proposed security scheme has been scrutinized in order to analyze its performance (and efficiency) in terms of overhead, increased jitter, and one-way delay variations.<p> Our implementation of the proposed security scheme utilized OpenVPN to provide privacy, integrity and authentication to the video streaming captured by Veracity’s clients and stored in Veracity’s proprietary NAS device (COLDSTORE). Utilization of OpenSSL FIPS Object module develops our security scheme in a FIPS compliant solution. For testing purposes, we created different test scenarios and collected data about the total delivery time of a video file, delivered from the IPCamera/NVR/DVR devices to the COLDSTORE device, the network overhead and lastly the one-way delay between the two endpoints. Another area of interest that we focus on is how to deploy certificates to new, existing, and replacement devices; and how this deployment may affect the system’s security design. In addition, we investigate the problems arising when a secured video stream needs to be played back via another device outside of our system’s network.The results of the thesis will be used as an input for product development activities by the company that hosted this thesis project. / Videoövervakningssystem är en växande industri. Precis som med de flesta systemen, har denna teknologi både möjligheter och risker. Den stora utspridningen av videoövervarkningssystemen har lett till essentiella säkerhetsrisker. Det ligger en stor vikt i att hantera säkerhetsrisker för videoövervakningssystem i och med att dessa system kan eventuellt fånga upp personlig data och därav attrahera attacker. Idag har nästan alla enheter blivit nätverksanslutna (eller är påväg att bli), vilket har lett till att avlyssning har blivit en vanlig attack. En avlyssnare kan exploatera en säkerhetsrisk och resultera i informationsläckor till obehöriga, videomanipulering, störningar, och reducerad prestanda i systemet. Det viktigt att videoövervakningssystem är standardiserade av lämpliga standardiseringsorganisationer för att säkra en hög kvalité i säkerhetstjänsterna som använder sig av dessa system och för att försäkra sig om kompatibilitet.<p> I den här examensarbetet studerade man regler och förordningar som har att göra med säkrandet av personlig data, för att kunna definiera kraven för det föreslagna robusta och högkvalitativa säkerhetsarkitekturen som skall integreras med videoövervakningssystemen.  Säkerhetsarkitekturen erbjuder United States Federal Information (FIPS)* kompatibla säkerhetstjänster genom att säkra kommunikationskanalen mellan systemets enheter.  Autentiseringen av systemets enheter sker genom att använda certifikat och nyckelutbyten.  Det föreslagna säkerhetsarkitekturen har granskats för att analysera dess prestanda vad gäller ineffektiviteter, ökade störningar och fördröjningar i envägs variationer. Vår genomförandet av den föreslagna systemet utnyttjas OpenVPN att tillhandahålla sekretess, integritet och autentisering till strömmande video fångades av Veracity kunder och lagras i Veracity egenutvecklade NAS-enhet (COLDSTORE). Utnyttjande av OpenSSL FIPS Objekt modulen utvecklar vår trygghet i ett FIPS-kompatibel lösning. För teständamål, skapade vi olika testscenarier och insamlade data om den totala leveranstiden för en videofil, som levereras från IPCamera / NVR / DVR-enheter till fryshus enhet, nätverket overhead och slutligen den enkelriktad fördröjning mellan de två ändpunkterna. Ett annat område av intresse som vi fokuserar på är certifikat för nya, existerande och ersättningsenheter; och hur det kan påverka systemets säkerhetsarkitektur. Utöver detta undersöker vi problemen som uppstår när en säkrad videoström behöver spelas upp i en enhet utanför systemets nätverk. Insatsen gjord i det här examensarbetet kommer användas som grund för produktutvecklingen av företaget där examensarbetet gjordes.