Deep Learning Hyper Parameter Optimization for Video Analytic in Centralized System

A framework to perform video examination is proposed utilizing a powerfully tuned convolutional arrange. Recordings are gotten from distributed storage, preprocessed, and a model for supporting order is created on these video streams utilizing cloud-based framework. A key spotlight in this paper is...

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Published in:International journal of engineering and advanced technology Vol. 9; no. 1; pp. 7300 - 7305
Main Authors: V., Arun, Bhattacharjee, Shuvam, Khandelwal, Ritik, Malik, Kanishk
Format: Journal Article
Language:English
Published: 30.10.2019
ISSN:2249-8958, 2249-8958
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Abstract A framework to perform video examination is proposed utilizing a powerfully tuned convolutional arrange. Recordings are gotten from distributed storage, preprocessed, and a model for supporting order is created on these video streams utilizing cloud-based framework. A key spotlight in this paper is on tuning hyper-parameters related with the profound learning calculation used to build the model. We further propose a programmed video object order pipeline to approve the framework. The scientific model used to help hyper-parameter tuning improves execution of the proposed pipeline, and results of different parameters on framework's presentation is analyzed. Along these lines, the parameters that contribute toward the most ideal presentation are chosen for the video object order pipeline. Our examination based approval uncovers an exactness and accuracy of 97% and 96%, separately. The framework demonstrated to be adaptable, strong, and adjustable for a wide range of utilizations.
AbstractList A framework to perform video examination is proposed utilizing a powerfully tuned convolutional arrange. Recordings are gotten from distributed storage, preprocessed, and a model for supporting order is created on these video streams utilizing cloud-based framework. A key spotlight in this paper is on tuning hyper-parameters related with the profound learning calculation used to build the model. We further propose a programmed video object order pipeline to approve the framework. The scientific model used to help hyper-parameter tuning improves execution of the proposed pipeline, and results of different parameters on framework's presentation is analyzed. Along these lines, the parameters that contribute toward the most ideal presentation are chosen for the video object order pipeline. Our examination based approval uncovers an exactness and accuracy of 97% and 96%, separately. The framework demonstrated to be adaptable, strong, and adjustable for a wide range of utilizations.
Author Malik, Kanishk
Khandelwal, Ritik
V., Arun
Bhattacharjee, Shuvam
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CorporateAuthor Assistant Professor, Department of Computer Science Engineering,SRM Institute of Science & Technology, Chennai, India
Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India
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