Basketball Tracking using Mean Shift Algorithm
G.Simi Margarat1, S.Sivasubramanian2
1G.Simi Margarat, M.Tech degree in Computer Science & Engineering From Madras University and Bharath University, Chennai, Tamil nadu India.
2S.Sivasubramanian, M.Tech. degree in Computer Science & Engineering From Madurai Kamaraj University,Madurai and Bharath University ,Chennai, Tamil nadu. India
Manuscript received on 11 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 339-344 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4159098319/19©BEIESP | DOI: 10.35940/ijrte.C4159.098319
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© 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: