A distributed big data analytics model for people re-identification based dimensionality reduction Online publication date: Thu, 25-Nov-2021
by Abderrahmane Ez-Zahout
International Journal of High Performance Systems Architecture (IJHPSA), Vol. 10, No. 2, 2021
Abstract: Big data analytics is a vast domain includes intelligent processing systems. Intelligent video surveillance generates a huge volume of data; and unstructured data requires fast processing speed. In big data analytics, most of the data involved in the processing comes from closed-circuit television (CCTV) are unstructured. Therefore, a very big volume of data requires an efficient and advanced processing. Those systems operate on four phases, detection, tracking, profile analysis and re-identification. In this work, re-identification is based real time dimensionality reduction with SparkMlLib library to speed up the feature's extraction. Practically, Minkowski distance and Kmeans algorithms are used for this issue. Therefore, to improve the effectiveness of our model, principal component analysis (PCA), cumulative match curve (CMC) and cumulative distribution function (CDF) have been used. These functions measure the re-identification errors and provide more re-identification in real time context.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Systems Architecture (IJHPSA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com