Multi-agent Based Distributed Video Surveillance System over IP

Wu, Chao; Jun, Xin

doi:10.1109/iscsct.2008.118

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…However, their work does not support distributed computing. Chao and Jun [18] introduced a distributed real-time video surveillance framework, which is employed to support the analysis of numerous surveillance data. In Reference [19], distributed real-time video processing is developed for objects and event detection but this work is limited to fewer applications.…”

Section: Related Workmentioning

confidence: 99%

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Uddin

Alam

et al. 2019

Symmetry

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In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

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Section: Related Workmentioning

confidence: 99%

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Uddin

Alam

et al. 2019

Symmetry

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“…Chao and Jun presented a multi-agent, distributed, video surveillance architecture. 8 Each terminal acts as an agent that locally processes the raw information from the sensors it governs, and only condensed results are transmitted through an IP network to reduce unnecessary load. The use of an active ‘blackboard’ is advocated as the data exchange center among information processing and control units, to ensure that all control information can be transmitted and exchanged when needed.…”

Section: Related Workmentioning

confidence: 99%

“…7 Chao and Jun presented a multi-agent distributed surveillance system with each surveillance terminal acting as an agent that processes raw information from the sensors it governs, and transmits condensed results through an IP network. 8 Aguilar-Ponce et al developed a scheme where cooperative agents perform object detection and tracking using clusters of wireless visual sensors. 9 The goals of current research are to develop algorithms that attract the attention of a human operator in real-time based on end-user requirements, process information arriving from a multi-sensor environment at high rates, and use low-cost standard components.…”

Section: Introductionmentioning

confidence: 99%

Efficient and effective automated surveillance agents using kernel tricks

et al. 2012

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This article was published in the SIMULATION [© 2012 The Society for Modeling and Simulation International.] and the definite version is available at: http://doi.org/10.1177/0037549712460908 The Article's website is at: http://sim.sagepub.com/content/89/5/562Many schemes have been presented over the years to develop automated visual surveillance systems. However, these schemes typically need custom equipment, or involve significant complexity and storage requirements. In this paper we present three software-based agents built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Kernel machines provide a powerful data mining technique that may be used for pattern matching in the presence of complex data. They work by first mapping the raw input data onto a (often much) higher-dimensional feature space, and then clustering in the feature space instead. The reasoning is that mapping onto the (higher-dimensional) feature space enables the comparison of additional, higher-order correlations in determining patterns between the raw data points. The agents proposed here have been built using algorithms that are adaptive, portable, do not require any expensive or sophisticated components, and are lightweight and efficient having run times of the order of hundredths of a second. Through application to real image streams from a simple, run-of-the-mill closed-circuit television surveillance system, and direct quantitative performance comparison with some existing schemes, we show that it is possible to easily obtain high detection accuracy with low computational and storage complexities.Publishe

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“…With the continuing advancement of computer vision algorithms and the scaling-down of electronic components, more and more smart, compact and energy-efficient surveillance systems have been designed and manufactured [1][2][3][4][5]. However for specific indoor intrusion applications, most traditional approaches cannot meet all the critical requirements at the same time.…”

Section: Introductionmentioning

confidence: 99%