A video cloud platform combing online and offline cloud computing technologies

Zhang, Weishan; Xu, Liang; Duan, Pengcheng; Gong, Wenjuan; Lu, Qinghua; Yang, Su

doi:10.1007/s00779-015-0879-3

Cited by 43 publications

(25 citation statements)

References 17 publications

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“…In academic research, Zhang et al [7] introduced a cloud-based architecture that can provide both real-time processing and offline batch data analysis of large-scale videos. This work explored Apache Kafka and Storm for real-time processing, while Hadoop based MapReduce framework is used for batch video data processing.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 10c,d shows real-time face recognition time in (ms) with varying number of cameras and nodes, and real-time action recognition time in (ms) with varying number of cameras and nodes respectively. Furthermore, we also compared the proposed framework face recognition time with video cloud platform [7]. In this experiment, for group 1 (3 node and 3 camera streams) our platform takes 34.6 milliseconds while video cloud platform [7] takes 132.45 milliseconds and similarly, for group 3 (4 node and 10 camera streams) our platform takes 138.4 milliseconds while video cloud platform [7] takes 423.8 milliseconds to recognize face on each frame.…”

Section: Online Service Scenario Applicationsmentioning

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: Online Service Scenario Applicationsmentioning

confidence: 99%

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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“…Zhang et al [30] proposed a comprehensive cloud-based architecture and a platform for batch processing integrated with fast processing that can provide intelligent analysis and storage with a robust solution for a large amount of video data. Kim et al [31] presented a Hadoop-based Distributed Video Transcoding System in a cloud computing environment that is used to transform various video codec formats into the MPEG-4 video format.…”

Section: Related Workmentioning

confidence: 99%

Similarity Estimation for Large-Scale Human Action Video Data on Spark

Uddin

Dolgorsuren

et al. 2018

Applied Sciences

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Abstract:The amount of human action video data is increasing rapidly due to the growth of multimedia data, which increases the problem of how to process the large number of human action videos efficiently. Therefore, we devise a novel approach for human action similarity estimation in the distributed environment. The efficiency of human action similarity estimation depends on feature descriptors. Existing feature descriptors such as Local Binary Pattern and Local Ternary Pattern can only extract texture information but cannot obtain the object shape information. To resolve this, we introduce a new feature descriptor, namely Edge based Local Pattern descriptor (ELP). ELP can extract object shape information besides texture information and ELP can also deal with intensity fluctuations. Moreover, we explore Apache Spark to perform feature extraction in the distributed environment. Finally, we present an empirical scalability evaluation of the task of extracting features from video datasets.

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“…MISPs need the information of users to provide optimal feedbacks to their subscribers. In order to ensure high-quality service, MISPs will analyze personal and business data [7,8]. Therefore, a functional stream data processing is vital to deal with the explosive data flooding.…”

Section: Introductionmentioning

confidence: 99%

A dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services

Yan

Wang²,

et al. 2016

Pers Ubiquit Comput

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As the huge number of mobile devices (e.g., smart phones, tablets and netbooks) increases, more and more people choose to use the Internet services financed by mobile Internet service providers (MISPs). To provide better services, it is quite necessary for MISPs to analyze the information hidden in the big data stream generated by users. Therefore, processing the real-time big data stream efficiently has become increasingly important. However, traditional static data storage technology fails to meet the demands of real-time data processing. To improve processing capacity, many parallel processing structures are proposed, which brings up the problem about how the parallel devices can be scheduled to maximize their efficiency. Accordingly, a dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services is proposed, and a stream query graph is built to calculate the weight of every edge. The edge with the minimum weight is selected to send tuples. Simulation results show that the proper number of the logic devices can dramatically reduce system response time. Furthermore, system context switching is reduced by increasing the number of tuples sent each time.

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A video cloud platform combing online and offline cloud computing technologies

Cited by 43 publications

References 17 publications

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Similarity Estimation for Large-Scale Human Action Video Data on Spark

A dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services

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