Paper Info
Title
Spatial frequency based video stream analysis for object classification and recognition in clouds.
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 through-put when tested on a number of video streams using cloud infrastructure.
Year
DOI
Venue
2016
10.1145/3006299.3006322
Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Keywords
Field
DocType
Empirical Mode Decomposition, Local Ternary Patterns, Riesz Transform, Amplitude Spectrum, Cloud Computing, Big Data Analytics, Object Classification
Data mining,Computer science,Artificial intelligence,Video quality,Video compression picture types,Local ternary patterns,Computer vision,Gaussian blur,Feature extraction,Video tracking,Video denoising,Machine learning,Hilbert–Huang transform
Conference
ISBN
Citations 
PageRank 
978-1-5090-4468-9
3
0.45
References 
Authors
12
3
Name
Order
Citations
PageRank
Muhammad Usman Yaseen1253.42
Ashiq Anjum233338.33
Nick Antonopoulos353148.72