Deep Learning Hyper Parameter Optimization for Video Analytic in Centralized System
Arun V.1, Shuvam Bhattacharjee2, Ritik Khandelwal3, Kanishk Malik4
1Arun V. , Assistant Professor, Department of Computer Science Engineering, SRM Institute of Science & Technology, Chennai, India.
2Shuvam Bhattacharjee, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
3Ritik Khandelwal, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
4Kanishk Malik, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7300-7305 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1215109119/2019©BEIESP | DOI: 10.35940/ijeat.A1215.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Automatic object classification, Cloud computing, Deep learning, Video analytics.