Video Analytics - Killer App for Edge Computing

Ananthanarayanan, Ganesh; Bahl, Victor; Cox, Landon P.; Crown, Alex; Nogbahi, Shadi; Shu, Yuanchao

doi:10.1145/3307334.3328589

Cited by 43 publications

(31 citation statements)

References 3 publications

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“…The Azure Video Analytics [53], Citilog [54], CheckVideo [13], and IntelliVision [14] support real-time IVA, and one can subscribe to the video stream to existing IVA services. The proposed system shares some features of video data acquisition of the CVAS proposed in [10,11,22,27].…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, Lin, C.-F. et al [25] implemented a prototype of a cloud-based video recorder system. A CVAS was proposed by Ananthanarayanan et al [26,27] called Killer App to meet the real-time demands of video analytics and to address latency, bandwidth, and provisioning challenges. Furthermore, various leading industrial CVAS have successfully been deployed, and they allow the consumers a service subscription.…”

Section: Related Workmentioning

confidence: 99%

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TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Alam

Lee

2020

Sensors

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In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Alam

Lee

2020

Sensors

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“…IVA has been extensively used in traffic control and transportation, e.g., lane traffic counts, incident detection, illegal u-turn, and many more. One of the main reason for deaths and injuries are trafficrelated misfortunes [335]. Proactive analytics is required to predict abnormal events so that to minimize or avoid such accidents.…”

Section: A Scholarly Cvasmentioning

confidence: 99%

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

2020

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The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. INDEX TERMS big data, intelligent video analytics, cloud-based video analytics system, video analytics survey, deep learning, distributed computing, intermediate results orchestration, cloud computing.

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“…For instance, the state of California implemented a NFC ticketing system, to reduce paper waste generated by commuters. Smart traffic management systems using computer vision and artificial intelligence, have been proposed in [ 37 ] where cameras could be used to detect wheel-chair users and provide them with more time to get across a road crossing.…”

Section: Devicesmentioning

confidence: 99%

UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices

Seljeseth

Yamin

Katt

2020

Sensors

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Information Technology (IT) has become an essential part of our lives and due to the emergence of the Internet-of-Things (IoT), technology has encompassed a majority of things that humans rely on in their daily lives. Furthermore, as IT becomes more relevant in daily lives, the need for IT to serve public emergency services has become more important. However, due to the infancy status of IoT, there is a need for a data consortium that would prove to be best used in servicing policing in a technological driven society. This paper will discuss the plausibility of creating a universal format for use in carrying out public services, such as emergency response by the police and regular law maintenance. In this research we will discuss what the police requires in their line-of-duty and how smart devices can be used to satisfy those needs. A data formatting framework is developed and demonstrated, with the goal of showing what can be done to unifying data from smart city sensors.

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Video Analytics - Killer App for Edge Computing

Cited by 43 publications

References 3 publications

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices

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