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    Spatial frequency based video stream analysis for object classification and recognition in clouds

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    Authors
    Yaseen, Muhammad Usman
    Anjum, Ashiq cc
    Antonopoulos, Nikolaos
    Affiliation
    University of Derby
    Issue Date
    2016-12-07
    
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    Abstract
    The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high throughput when tested on a number of video streams using cloud infrastructure.
    Citation
    Yaseen, M. U. (2016) 'Spatial frequency based video stream analysis for object classification and recognition in clouds' Proceedings of the 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies, Shanghai: China, 6-9 December
    Publisher
    IEEE
    Journal
    Proceedings of the 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies
    URI
    http://hdl.handle.net/10545/620948
    Additional Links
    http://computing.derby.ac.uk/bdcat2016/
    Type
    Meetings and Proceedings
    Language
    en
    Collections
    Department of Electronics, Computing & Maths

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