Abstract
Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of distributed processing for training and inference. We propose a cloud‐based video analytics system based on an optimally tuned convolutional neural network to classify objects from video streams. The tuning of convolutional neural network is empowered by in‐memory distributed computing. The object classification is performed by comparing the target object with the prestored trained patterns, generating a set of matching scores. The matching scores greater than an empirically determined threshold reveal the classification of the target object. The proposed system proved to be robust to classification errors with an accuracy and precision of 97% and 96%, respectively, and can be used as a general‐purpose video analytics system.Citation
Yaseen, M.U., Anjum A, Farid M, Antonopoulos N. (2018) 'Cloud‐based video analytics using convolutional neural networks', Software: Practice and Experience. DOI: 10.1002/spe.2636Journal
Software: Practice and ExperienceDOI
10.1002/spe.2636Additional Links
http://doi.wiley.com/10.1002/spe.2636Type
ArticleLanguage
enISSN
0038-0644ae974a485f413a2113503eed53cd6c53
10.1002/spe.2636