Modeling and analysis of a deep learning pipeline for cloud based video analytics.
Conference item
Yaseen, M., Anjum, Ashiq and Antonopoulos, Nikolaos 2017. Modeling and analysis of a deep learning pipeline for cloud based video analytics. https://doi.org/10.1145/3148055.3148081
| Authors | Yaseen, M., Anjum, Ashiq and Antonopoulos, Nikolaos |
|---|---|
| Abstract | Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. These systems necessitate massive amounts of labeled data and training time to perform fine tuning of hyper-parameters for object classification. 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 capable of enhancing its own training data by performing transformations including rotation, flip and skew on the input dataset making it more robust and self-adaptive. The use of in-memory distributed training mechanism rapidly incorporates large number of distinguishing features from the training dataset - enabling the system to perform object classification with least human assistance and external support. The validation of the system is performed by means of an object classification case-study using a dataset of 100GB in size comprising of 88,432 video frames on an 8 node cloud. The extensive experimentation reveals an accuracy and precision of 0.97 and 0.96 respectively after a training of 6.8 hours. The system is scalable, robust to classification errors and can be customized for any real-life situation. |
Video analytics systems based on deep learning approaches are | |
| Keywords | Video analysis; Deep learning; Cloud computing |
| Year | 2017 |
| Journal | Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2017) |
| Digital Object Identifier (DOI) | https://doi.org/10.1145/3148055.3148081 |
| Web address (URL) | http://hdl.handle.net/10545/622032 |
| http://creativecommons.org/licenses/by/4.0/ | |
| hdl:10545/622032 | |
| ISBN | 9781450355490 |
| File | File Access Level Open |
| File | File Access Level Open |
| File | File Access Level Open |
| Publication dates | 05 Dec 2017 |
| Publication process dates | |
| Deposited | 20 Dec 2017, 16:37 |
| Contributors | University of Derby, University of Derby, Derby, United Kingdom, University of Derby, Derby, United Kingdom and University of Derby, Derby, United Kingdom |
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