Dynamic Threshold for Resource Tracking in Observed Scenes

Olatunji, Iyiola E.; Cheng, Chun Hung

doi:10.1109/iisa.2018.8633691

Cited by 7 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth to mention that video surveillance cameras are highly pervasive. For example, 170 million cameras are in deployed in China roads only [222]. These devices are generating a huge amount of data as previously described.…”

Section: Intelligent Edge For Video Streamingmentioning

confidence: 99%

A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

et al. 2022

View full text Add to dashboard Cite

5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowdsourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-toend communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultrareliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-ofthe-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.

show abstract

Section: Intelligent Edge For Video Streamingmentioning

confidence: 99%

A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Along with these, various leading industrial organizations have successfully deployed video management and analytics platforms that provide more bandwidth and high-resolution cameras collecting videos at scale and has become one of the latest trends in the video surveillance industry. For example, more than 400 hours of videos are uploaded in a minute on Youtube [5], and more than one hundred and seventy million video surveillance cameras have been installed in china only [6]. It has been reported that the data generated by various IoT devices will see a growth rate of 28.7% over the period 2018-2025, where surveillance videos are the majority shareholder [7].…”

Section: Introductionmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract

“…Various leading industrial organizations have successfully deployed video management and analytics platforms that provide more bandwidth and high-resolution cameras collecting videos at scale and has become one of the latest trends in the video surveillance industry. For example, YouTube users upload more than 400 h of videos per minute [1], and more than one hundred and seventy million video surveillance cameras have been installed in China only [2]. It has been reported that the data generated by Internet of things (IoT) devices will see a growth rate of 28.7% over the period 2018-2025, where surveillance videos are the majority shareholder, i.e., 65% [3].…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Dynamic Threshold for Resource Tracking in Observed Scenes

Cited by 7 publications

References 10 publications

A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

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

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

Contact Info

Product

Resources

About