High Performance Video Stream Analytics System for Object Detection and Classification


Yaseen, Muhammad Usman 2019. High Performance Video Stream Analytics System for Object Detection and Classification. Thesis
AuthorsYaseen, Muhammad Usman
Qualification namePhD

Due to the recent advances in cameras, cell phones and camcorders, particularly the resolution at which they can record an image/video, large amounts of data are generated daily. This video data is often so large that manually inspecting it for object detection and classification can be time consuming and error prone, thereby it requires automated analysis to extract useful
information and meta-data. The automated analysis from video streams also comes with numerous challenges such as blur content and variation in illumination conditions and poses. We investigate an automated video analytics system in this thesis which takes into account the characteristics from both shallow and deep learning domains. We propose fusion of features
from spatial frequency domain to perform highly accurate blur and illumination invariant object classification using deep learning networks. We also propose the tuning of hyper-parameters associated with the deep learning network through a mathematical model. The mathematical model used to support hyper-parameter tuning improved the performance of the proposed system during training. The outcomes of various hyper-parameters on system's performance are compared. The parameters that contribute towards the most optimal performance are selected for the video object classification. The proposed video analytics system has been demonstrated to process a large number of video streams and the underlying infrastructure is able to scale based on the number and size of the video stream(s) being processed. The extensive experimentation on publicly available image and video datasets reveal that the proposed system is significantly more accurate and scalable and can be used as a general purpose video analytics system.

KeywordsVideo analytics, Cloud computing, Object classification
PublisherUniversity of Derby
College of Engineering and Technology
Web address (URL)http://hdl.handle.net/10545/623640
File Access Level
AdvisorAnjum, Ashiq
Publication process dates
Deposited26 Mar 2019, 16:07
Publication dates05 Feb 2019
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