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Modeling and Analysis of a Deep Learning Pipeline for Cloud based Video Analytics

Muhammad Usman Yaseen, Ashiq Anjum, Nick Antonopoulos
2017 Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '17  
We propose a cloud based video analytics system built upon an optimally tuned deep learning model to classify objects from video streams.  ...  The tuning of the hyper-parameters including learning rate, momentum, activation function and optimization algorithm is optimized through a mathematical model for efficient analysis of video streams.  ...  The system is built upon a deep learning model whose optimization is inspired by a mathematical function for efficient analysis of video streams.  ... 
doi:10.1145/3148055.3148081 dblp:conf/bdc/YaseenAA17 fatcat:7ph25pho3jgxfa7nf5usekqghu

Edge Enhanced Deep Learning System for Large-Scale Video Stream Analytics

Muhammad Ali, Ashiq Anjum, M. Usman Yaseen, A. Reza Zamani, Daniel Balouek-Thomert, Omer Rana, Manish Parashar
2018 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC)  
Applying deep learning models to large-scale IoT data is a compute-intensive task and needs significant computational resources.  ...  We address this problem by distributing the deep learning pipeline across edge and cloudlet/fog resources.  ...  A typical Cloud-based deep learning pipeline for object recognition and the proposed edge enhanced decomposition of the pipeline is shown in Fig.2 . It consists of four stages marked as S1-S4.  ... 
doi:10.1109/cfec.2018.8358733 dblp:conf/icfec/AliAYZBRP18 fatcat:3g5mvxvwkfdwhob6dmersotd7u

Edge-based Video Analytic for Smart Cities

Dipak Pudasaini, Abdolreza Abhari
2021 International Journal of Advanced Computer Science and Applications  
The problems of cloudbased approach for video analytic are high latency and more network bandwidth to transfer data into the cloud.  ...  To overcome these problems, we propose a model based on dividing the jobs into smaller sub-tasks with less processing requirements in a typical video analytics application for the development of smart  ...  We simulated our model on large number of fog devices using iFogSim simulator, but we tested a smaller number of videos in CNN-based model. This is a limitation of this work.  ... 
doi:10.14569/ijacsa.2021.0120701 fatcat:mj7urnr2o5hg7k6japmcml32nq

Video Analytics - Killer App for Edge Computing

Ganesh Ananthanarayanan, Victor Bahl, Landon Cox, Alex Crown, Shadi Nogbahi, Yuanchao Shu
2019 Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys '19  
The video pipeline consists of multiple modules including the decoder, background subtractor, and deep neural network (DNN) models.  ...  We highlight three main aspects. 1) Our hybrid architecture intelligently splits the video analytics between the edge and the cloud, along with a cascaded mode of operation that uses CPU-based operators  ... 
doi:10.1145/3307334.3328589 dblp:conf/mobisys/Ananthanarayanan19 fatcat:6x4vluhannfafbotbjsdbxfewm

Video Big Data Analytics in the Cloud: Research Issues and Challenges [article]

Aftab Alam, Shah Khalid, Muhammad Numan Khan, Tariq Habib Afridi, Irfan Ullah, Young-Koo Lee
2020 arXiv   pre-print
This study proposes a service-oriented layered reference architecture for intelligent video big data analytics in the cloud.  ...  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.  ...  Video Ontology is a generic semantic-based model for the representation and organization of video resources that allow the CVAS users for contextual complex, event analysis, reasoning, search, and retrieval  ... 
arXiv:2011.02694v1 fatcat:gbshlwsli5bxtnvnbpcdzvnzca

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

Aftab Alam, Irfan Ullah, Young-Koo Lee
2020 IEEE Access  
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  ...  We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud.  ...  and notable company in the field of Artificial Intelligence (AI) and deep learning-based video analytics and video cloud platform.  ... 
doi:10.1109/access.2020.3017135 fatcat:qc62bhzlrfcwblnvurb5okfjxe

Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Alessandro Sassu, Jose Francisco Saenz-Cogollo, Maurizio Agelli
2021 Sensors  
In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning.  ...  Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the  ...  to easily include and deploy new deep learning models using the most mature deep learning frameworks; • Standard HTTP and WebRTC APIs for providing web-based video analytics services and allowing the  ... 
doi:10.3390/s21124045 fatcat:tvj7bsrjzjcddlsjiqqro6oosa

Enabling microservices management for Deep Learning applications across the Edge-Cloud Continuum

Zeina Houmani, Daniel Balouek-Thomert, Eddy Caron, Manish Parashar
2021 2021 IEEE 33rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)  
We demonstrate that in a multi-user scenario, with a standard frame rate of 25 frames per second, the system speed-up data analysis up to 54.4% compared to a Cloud-only-based scenario with an analysis  ...  However, current data analysis rarely manages the entire Deep Learning pipeline along the data path, making it complex for developers to implement strategies in realworld deployments.  ...  ACKNOWLEDGMENTS This research is supported in part by the NSF under grants numbers OAC 1640834, OAC 1835692, and OCE 1745246.  ... 
doi:10.1109/sbac-pad53543.2021.00025 fatcat:oud6hcplpnbn5eydwm6up2azvq

Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds

Muhammad Usman Yaseen, Ashiq Anjum, Omer Rana, Nikolaos Antonopoulos
2018 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Videos are fetched from cloud storage, pre-processed and a model for supporting classification is developed on these video streams using cloud-based infrastructure.  ...  A key focus in this work is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model.  ...  Deep learning based Video analytics systems can involve many hyper-parameters, including learning rate, activation function and weight parameter initialization.  ... 
doi:10.1109/tsmc.2018.2840341 fatcat:y6fp7xy6ufen5pae4qjctuc6au

Live Video Analytics with FPGA-based Smart Cameras

Shang Wang, Chen Zhang, Yuanchao Shu, Yunxin Liu
2019 Proceedings of the 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges - HotEdgeVideo'19  
The increasing complexity of deep learning and massive deployment of cameras at the edge have drastically increased the resource demand of edge data analytics.  ...  We envision a novel analytics stack that orchestrates the computing resource of massive networked cameras to enable efficient edge video analytics.  ...  Finally, many video analytics applications use complex pipeline of multiple models to analyze a video stream.  ... 
doi:10.1145/3349614.3356027 dblp:conf/mobicom/WangZSL19 fatcat:575dyoeos5e35oxkephcveww2m

A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems [article]

Nada Ibrahim, Preeti Maurya, Omid Jafari, Parth Nagarkar
2021 arXiv   pre-print
Neural networks are the state-of-the-art for performing video analytics tasks such as video annotation and object detection.  ...  the performance of Neural Network-Based Video Analytics Systems.  ...  Optimization-based Video Analytics Techniques Traditional and naive video analytics systems require multiple queries to the learned deep models and suffer from great computational costs.  ... 
arXiv:2105.14195v1 fatcat:klq3urgjsjhb7ef7qfdp6dly3u

Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras [article]

Pratik Dubal, Rohan Mahadev, Suraj Kothawade, Kunal Dargan, Rishabh Iyer
2018 arXiv   pre-print
By sharing our implementation details and the experiences learned from deploying customized deep learning models for various customers, we hope that customized deep learning based video analytics is widely  ...  This paper demonstrates the effectiveness of our customized deep learning based video analytics system in various applications focused on security, safety, customer analytics and process compliance.  ...  Deep learning has dominated the landscape of computer vision for the past few years, and almost all video analytics applications can be solved with high accuracies via deep learning.  ... 
arXiv:1805.10604v2 fatcat:xzckmwvyv5hvfik35awnvwc2ji

A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning [article]

Huaizheng Zhang, Meng Shen, Yizheng Huang, Yonggang Wen, Yong Luo, Guanyu Gao, Kyle Guan
2021 arXiv   pre-print
VPaaS adopts serverless computing to enable developers to build a video analytics pipeline by simply programming a set of functions (e.g., model inference), which are then orchestrated to process videos  ...  To the best of our knowledge, this paper presents the first serverless system that takes full advantage of the client-fog-cloud synergy to better serve the DNN-based video analytics.  ...  Fig. 2 : The example video analysis pipelines. In general, a pipeline consists of two stages, quality control and content analysis. A video stream is re-encoded for bandwidth efficiency.  ... 
arXiv:2102.03012v1 fatcat:q4mxkmjkqbhq7m2cfq5of5i5p4


Zheng Yang, Xiaowu He, Jiaxing Wu, Xu Wang, Yi Zhao
2021 Scientia Sinica Informationis  
In: Tsihrintzis G A, Virvou M, Sakkopoulos E, et al, eds., Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems, Learning and Analytics in Intelligent Systems.  ...  Figure 5 5 Video analytics pipeline for AR applications, where the video source and rendering part must be on the end devices Figure 8 8 Multi Figure 9 " 9 Detect and track" framework based on computation  ... 
doi:10.1360/ssi-2021-0133 fatcat:qs7jnvnknjhdrhfrru6rfbwuge

LIMPID/BisQue: A scalable infrastructure for reproducible, image driven data science

B.S. Manjunath
2018 Figshare  
summarized data. learning method development (Use Case: live Calcium imaging in neurons) • LIMPID is built on cloud-based analysis platform BisQue • Management, analysis, and sharing of images and  ...  • Automated identification of neurons from background based on shape, position and fluorescent changes. • Deep  ... 
doi:10.6084/m9.figshare.6171392 fatcat:nxopoayxy5e6rco3k2fuhjyeum
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