Efficient Video Surveillance: Performance Evaluation in Distributed Video Surveillance Systems

Karimaa, Aleksandra

doi:10.5772/15259

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…OurCVSSsystemutilizesASP.NETtodeploytheB/Smodelbasedonthreelayers:clientlayer, weblayeranddatalayer (Xiong,etal.,2014).TheclientlayertakesuseoftheSaaStoaccessthesite forthepurposeofHumanComputerInteraction(HCI) (Prati,etal.,2013).Theweblayerliesbetween theclientlayerandthedatalayeraccrossingthewebserverandtheapplicationserver.Thedatalayer isthebasetieroftheCVSSsystem,whichwasdesignedespeciallyforhugedatastorage.ASP.NET supports three types of authentication (Karimaa, 2011), namely, windows authentication, forms authentication,andpassportauthentication.OurCVSSsystemappliescookiesastheauthentication mechanismwhichisbasedonformauthentication.Acookieisasmallpieceofbinaryinformation whichrespondstotheuserrequestsforthepurposeofauthentication.IntheCVSSloginpage,once ausersubmitsinformationpackagetothecloudserver,ASP.NETwillsendanewcookietoconfirm theeffectivenessofanidentityticket.Ifauserpassestheauthentication,anewrecordwillbeinserted intothedatabase.Whileauserisintheoperatingprocess,therecorderwillupdatethedatabasetimely shownasFigure1.InCVSSS,wehavedesignedthefeatureslike,…”

Section: System Designmentioning

confidence: 99%

CVSS

Zhou

Yan

Shu

et al. 2018

International Journal of Digital Crime and Forensics

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A large amount of surveillance videos and images need sufficient storage. In this article, an architecture of cloud-based surveillance systems and its modules will be designed, the Cloud-based Visual Surveillance System (CVSS) will be implemented on a private cloud using a Virtual Machine (VM). The users are able to link their cameras to the CVSS system so that the goal of this design can be achieved. The authors' CVSS system is able to push notification messages of captured videos to receivers, and their users could receive a surveillance video along with its events. The CVSS system fully makes use of the merits of cloud computing, which make it more advanced as stated in the evaluation section of this article. The contributions of this article are to be implemented in the CVSS system with: (1) video stream input, (2) intelligent visual surveillance, (3) real-time video transcoding and storage, (4) message pushing and media streaming output.

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Section: System Designmentioning

confidence: 99%

CVSS

Zhou

Yan

Shu

et al. 2018

International Journal of Digital Crime and Forensics

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“…The parameter  controls the relative importance of Recall and Precision. In a majority of the surveillance applications Recall and Precision are given equal importance by setting  =0.5 [56,57]. Putting  =0.5 in above equation we get F -measure = 2.…”

Section: Subjective Evaluation Of the Proposed Techniquementioning

confidence: 99%

Saliency-directed prioritization of visual data in wireless surveillance networks

et al. 2015

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Item Type Article Authors Mehmood, Irfan; Sajjad, M.; Ejaz, W.; Baik, S.W. Citation Mehmood I, Sajjad M, Ejaz W et al (2015) Saliency-directed prioritization of visual data in wireless surveillance networks. Information Fusion. 24: 16-30. RightsAbstract: In wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos causes intermittency on the analysts' ability to extract actionable intelligence from it. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of the traditional WVSN. The proposed framework selects semantically relevant information before transmission to the sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates background bootstrapping, which helps to increase the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified into a high or a low-priority node. This classification is dynamic such that camera nodes toggle between high-priority and low-priority status depending on the coverage of region of interest. The high-priority camera nodes are allowed to access the reliable radio channels for the sake of timely and reliable transmission of data. We compared the performance of this framework with other state-of-the-art methods in the cases of both single and multi-camera monitoring. The results indicate the usefulness of the proposed method in terms of salient event coverage and the reduction of computational and transmission costs as well as in helping analysts find semantically relevant visual information.Keywords: image prioritization, wireless visual sensor networks, monitoring applications, salient activity detection. Corresponding Author networks. In contrast to scalar sensors, WVSNs capture visual data, which offers rich information. Moreover, owing to their high versatility, small size, and dense spatial coverage, WVSNs can be deployed flexibly in various applications, such as in remote patient monitoring, distributed multimedia-based surveillance, and security systems [1][2][3][4]. In such setups, visual data about the event area has tremendous potential to influence decision making because the WVSNs address real-time observation of the events occurring within a busy environment. Despite the existence of numerous ideas of attractive surveillance applications, their actual implementation using the WVSNs remains a challenge. This is a challenge particularly because surveillance systems consist of numerous sensors that consume a large amount of bandwidth to transmit their raw video streams. Further, these video streams require extensive processing to detect events and anomalies. Recently, there have been attempts to advance the current WVSN technologies by implementation of intelligent methods for automatically extracting the relevant data at the source node to reduce the consumption of network bandwidth.WVSN based mo...

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“…Smart camera networks have emerged as alternative to traditional CCTV for efficient multiple object tracking in large areas [5]. In such networks, the distributed nodes communicate to approach a comprehensive coverage of the area under monitoring; which not only improves the responsiveness of the system but also increases its flexibility.…”

Section: Introductionmentioning

confidence: 99%

Self-Coordinated Target Assignment and Camera Handoff in Distributed Network of Embedded Smart Cameras

Yonga

Alfredo²,

Mefenza

et al. 2014

Proceedings of the International Conference on Distributed Smart Cameras

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Tracking several objects across multiple cameras is essential for collaborative monitoring in distributed camera networks. The tractability of the related optimization aiming at tracking a maximal number of important targets, decreases with the growing number of objects moving across cameras. To tackle this issue, a viable model and sound object representation, which can leverage the power of existing tool at run-time for a fast computation of solution, is required.In this paper, we provide a formalism to object tracking across multiple cameras. A first assignment of objects to cameras is performed at start-up to initialize a set of distributed trackers in embedded cameras. We model the runtime self-coordination problem with target handover by encoding the problem as a run-time binding of objects to cameras. This approach has successively been used in high-level system synthesis. Our model of distributed tracking is based on Answer Set Programming, a declarative programming paradigm, that helps formulate the distribution and target handover problem as a search problem, such that by using existing answer set solvers, we produce stable solutions in real-time by incrementally solving time-based encoded ASP problems. The effectiveness of the proposed approach is proven on a 3-node camera network deployment.

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Efficient Video Surveillance: Performance Evaluation in Distributed Video Surveillance Systems

Cited by 6 publications

References 29 publications

CVSS

CVSS

Saliency-directed prioritization of visual data in wireless surveillance networks

Self-Coordinated Target Assignment and Camera Handoff in Distributed Network of Embedded Smart Cameras

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