Cloud-based scalable object detection and classification in video streams

Yaseen, Muhammad Usman, Anjum, Ashiq, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Hill, Richard 2018. Cloud-based scalable object detection and classification in video streams. Future Generation Computer Systems 80 , pp. 286-298. 10.1016/j.future.2017.02.003

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Abstract

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 useful content can be time consuming and error prone, thereby it requires automated analysis to extract useful information and metadata. Existing video analysis systems lack automation, scalability and operate under a supervised learning domain, requiring substantial amounts of labelled data and training time. We present a cloud-based, automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number and size of the stream(s) being considered. The system automates the video analysis process and reduces manual intervention. An operator using this system only specifies which object of interest is to be located from the video streams. Video streams are then automatically fetched from the cloud storage and analyzed in an unsupervised way. The proposed system was able to locate and classify an object of interest from one month of recorded video streams comprising 175 GB in size on a 15 node cloud in 6.52 h. The GPU powered infrastructure took 3 h to accomplish the same task. Occupancy of GPU resources in cloud is optimized and data transfer between CPU and GPU is minimized to achieve high performance. The scalability of the system is demonstrated along with a classification accuracy of 95%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Data Innovation Research Institute (DIURI)
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords:	Unsupervised object classification; Cloud computing; GPUs; High performance video analytics
Publisher:	Elsevier
ISSN:	0167-739X
Date of First Compliant Deposit:	20 April 2017
Date of Acceptance:	4 February 2017
Last Modified:	04 Dec 2024 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/98251

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