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Research Article | Open Access |

Video Streaming Analytics for Traffic Monitoring Systems

Author 1: Muhammad Arslan Amin Author 2: Muhammad Kashif Hanif Author 3: Muhammad Umer Sarwar Author 4: Muhammad Kamran Sarwar Author 5: Ayesha Kanwal Author 6: Muhammad Azeem
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 9, No. 11 · Published 2018

DOI: https://doi.org/10.14569/IJACSA.2018.091192

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Abstract

It is considered a difficult task to have check on traffic during rush hours. Traditional applications are man-ual, costly, time consuming, and the human factors involved. Large scale data is being generated from different resources. Advancement in technology make it possible to store, process, analyze, and communicate with large scale of video data. The manual applications are wiped out with the invention of automatic applications. Automatic video streaming analytics applications helps to reduce computational resources. The reason is cost efficient and accurate predictions while monitoring traffic on roads. This study reviews previously developed application of video streaming analytics for traffic monitoring systems using Hadoop that are able to efficiently analyze video streams.

Keywords

Video streaming analytics Traffic monitoring sys-tem Video streams Hadoop GPU CNN Deep learning

How to Cite this Article

Amin, M. A., Hanif, M. K., Sarwar, M. U., Sarwar, M. K., Kanwal, A., & Azeem, M. (2018). Video Streaming Analytics for Traffic Monitoring Systems. International Journal of Advanced Computer Science and Applications, 9(11). https://doi.org/10.14569/IJACSA.2018.091192

Amin, Muhammad Arslan, et al.. "Video Streaming Analytics for Traffic Monitoring Systems." International Journal of Advanced Computer Science and Applications, vol. 9, no. 11, 2018, https://doi.org/10.14569/IJACSA.2018.091192.

@article{Amin2018,
  title     = {Video Streaming Analytics for Traffic Monitoring Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {11},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Arslan Amin and Muhammad Kashif Hanif and Muhammad Umer Sarwar and Muhammad Kamran Sarwar and Ayesha Kanwal and Muhammad Azeem},
  doi       = {10.14569/IJACSA.2018.091192},
  url       = {https://doi.org/10.14569/IJACSA.2018.091192}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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