Multi Variant Feature Similarity Based Behavior Tracking in Video Surveillance using Ann
Shana L1, C Seldev Christopher2
1Shana L, Computer Science and Engineering, St. Xavier’s College of Engineering, Anna University, Nagercoil, India.
2Prof Dr. C Seldev Christopher, Computer Science and Engineering, St. Xavier’s College of Engineering, Anna University, Nagercoil, India
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1419-1423 | Volume-8 Issue-3 September 2019 | Retrieval Number: B3673078219/19©BEIESP | DOI: 10.35940/ijrte.B3673.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.
Keywords: Ensemble Classifiers, VGG16, VGG19, Resnet56, Probability Voting Technique, CIFAR-10.
Scope of the Article: Multi-Agent Systems