A distributed big data analytics model for people re-identification based dimensionality reduction

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. © 2021 Inderscience Enterprises Ltd.