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

Yaseen, Muhammad Usman; Anjum, Ashiq; Antonopoulos, Nick

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., Anjum, A., & Antonopoulos, N. (2016, December). Spatial frequency based video stream analysis for object classification and recognition in clouds. Presented at 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies, Shanghai, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies
• Start Date: Dec 6, 2016
• End Date: Dec 9, 2016
• Acceptance Date: Dec 6, 2016
• Publication Date: Dec 6, 2016
• Deposit Date: Feb 12, 2019
• Journal: Proceedings of the 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies
• Publisher: Association for Computing Machinery (ACM)
• Book Title: Proceedings of the 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies
• ISBN: 9781450346177
• DOI: https://doi.org/10.1145/3006299.3006322
• Keywords: Empirical Mode Decomposition; Local Ternary Patterns; Riesz Transform; Amplitude Spectrum, Cloud Computing, Big Data Analytics, Object Classification
• Public URL: http://researchrepository.napier.ac.uk/Output/1557175