Framework for a Cloud-Based Multimedia Surveillance System

Hossain, M. Shamim

doi:10.1155/2014/135257

Cited by 39 publications

(21 citation statements)

References 38 publications

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“…In Reference [19], distributed real-time video processing is developed for objects and event detection but this work is limited to fewer applications. Hossain [5] presented a cloud-based client-server framework for the surveillance system that includes segmentation, motion detection, tracking activity and recognition. Zhang et al [20] presented an online video surveillance framework that includes the distributed Kafka message queue and Spark Streaming; however, they overlooked the offline video processing.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, due to the large-scale video data, an elastic solution to save and process video data is needed for potential decision making. However, most of the existing works [5,6] rely on a traditional client/server framework to perform simple tasks (e.g., face and object recognition) [7] while lacks the support for more complex application scenarios (e.g., activity recognition). Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Uddin

Alam

et al. 2019

Symmetry

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“…The system is based on the Hadoop distributed file system to provide scalable video recording and backup capabilities. M. Anwar Hossain analyzed the suitability of cloud-based multimedia surveillance systems and proposed a cloud-based multimedia surveillance system framework [11][12][13]. The system can efficiently deal with system overload, meet the storage requirements of the large-scale monitoring system, and provide data access to users.…”

Section: Related Workmentioning

confidence: 99%

Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

Zhang

Shen

2018

Future Internet

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With the rapid development of video surveillance technology, especially the popularity of cloud-based video surveillance applications, video data begins to grow explosively. However, in the cloud-based video surveillance system, replicas occupy an amount of storage space. Also, the slow response to video playback constrains the performance of the system. In this paper, considering the characteristics of video data comprehensively, we propose a dynamic redundant replicas mechanism based on security levels that can dynamically adjust the number of replicas. Based on the location correlation between cameras, this paper also proposes a data cache strategy to improve the response speed of data reading. Experiments illustrate that: (1) our dynamic redundant replicas mechanism can save storage space while ensuring data security; (2) the cache mechanism can predict the playback behaviors of the users in advance and improve the response speed of data reading according to the location and time correlation of the front-end cameras; and (3) in terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods.

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“…Some other researches for smart city, surveillance, fire safety for home, and home monitoring application research topics are also discussed in recent years [26][27][28][29][30].…”

Section: Related Researches On Wireless Home Monitoringmentioning

confidence: 99%

Development of a Multisensor Embedded Intelligent Home Environment Monitoring System Based on Digital Signal Processor and Wi-Fi

Chen

Chang

Chen

2015

International Journal of Distributed Sensor Networks

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Wi-Fi has been widely adopted by families in Taiwan, for it has the advantage of designing family monitoring systems with its wide usage. Therefore, there are many advantages for designing family monitoring systems. Based on our previous research architecture, we implemented a monitoring system for family environments based on Wi-Fi and sensor technologies. Humidity, gas, smoke, and temperature sensors are applied for designing and developing the intelligent home environment monitoring system in this paper. A system prototype is proposed in this research, and the experimental results are discussed. The results showed that the system is workable and remarkable for environment monitoring. The contribution of this is to propose a framework for future home environment monitoring system developers to refer to.

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Framework for a Cloud-Based Multimedia Surveillance System

Cited by 39 publications

References 38 publications

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

Development of a Multisensor Embedded Intelligent Home Environment Monitoring System Based on Digital Signal Processor and Wi-Fi

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