Deep learning hyper-parameter optimization for video analytics in clouds.
Journal article
Yaseen, M., Anjum, Ashiq, Rana, Omer and Antonopoulos, Nikolaos 2018. Deep learning hyper-parameter optimization for video analytics in clouds. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/TSMC.2018.2840341
| Authors | Yaseen, M., Anjum, Ashiq, Rana, Omer and Antonopoulos, Nikolaos |
|---|---|
| Abstract | A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We further propose an automatic video object classification pipeline to validate the system. The mathematical model used to support hyper-parameter tuning improves performance of the proposed pipeline, and outcomes of various parameters on system's performance is compared. Subsequently, the parameters that contribute toward the most optimal performance are selected for the video object classification pipeline. Our experiment-based validation reveals an accuracy and precision of 97% and 96%, respectively. The system proved to be scalable, robust, and customizable for a variety of different applications. |
| Keywords | Automatic object classification; Cloud computing; Video analysis; Deep learning |
| Year | 2018 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics |
| Publisher | IEEE |
| ISSN | 21682216 |
| 21682232 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TSMC.2018.2840341 |
| Web address (URL) | http://hdl.handle.net/10545/622767 |
| hdl:10545/622767 | |
| Publication dates | 15 Jun 2018 |
| Publication process dates | |
| Deposited | 27 Jun 2018, 08:19 |
| Accepted | 09 May 2018 |
| Rights | Archived with thanks to IEEE Transactions on Systems, Man, and Cybernetics: Systems |
| Contributors | University of Derby and Cardiff University |
| File | File Access Level Open |
| File | File Access Level Open |
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