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Proceedings Paper

Understanding video transmission decisions in cloud based computer vision services
Author(s): Nijad Anabtawi; Rony M. Ferzli
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Paper Abstract

This paper presents a study about the effect of the quality of the input video source on the computer vision system robustness and how to make use of the findings to create a framework generating a set of recommendation or rules for researchers and developers in the field to use. The study is of high importance especially for cloud based computer vision platforms where the transmission of raw uncompressed video is not possible, as such it is desired to have a sweet spot where the usage of bandwidth is at optimal level while maintaining high recognition rate. Experimental results showed that creating such rules is possible and beneficial to integrate in an end to end cloud based computer vision service.

Paper Details

Date Published: 27 February 2015
PDF: 9 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050V (27 February 2015); doi: 10.1117/12.2083563
Show Author Affiliations
Nijad Anabtawi, Arizona State Univ. (United States)
Rony M. Ferzli, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)

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