Citation

Alam A, Lee YK. Sensors (Basel) 2020; 20(12).

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s20123581

PMID

32599912

Abstract

In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.

Language: en

Keywords

big data analytics; distributed video analytics; intermediate results orchestration; service-oriented architecture; video big data curation