Open AccessClassification of Rigid and Non-Rigid Objects Using CNN
Author(s) -
Aparna Gullapelly,
Barnali Gupta Banik
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350409
Subject(s) - artificial intelligence , convolutional neural network , computer science , python (programming language) , computer vision , rigid body , rigid transformation , pattern recognition (psychology) , set (abstract data type) , binary number , mathematics , physics , arithmetic , classical mechanics , programming language , operating system
Classifying moving objects in video surveillance can be difficult, and it is challenging to classify hard and soft objects with high Accuracy. Here rigid and non-rigid objects are limited to vehicles and people. CNN is used for the binary classification of rigid and non-rigid objects. A deep-learning system using convolutional neural networks was trained using python and categorized according to their appearance. The classification is supplemented by the use of a data set, which contains two classes of images that are both rigid and not rigid that differ by illuminations.