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A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos

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A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos


Akintola Kolawole G.

https://doi.org/10.31142/ijtsrd109



Akintola Kolawole G. "A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4, June 2017, URL: https://www.ijtsrd.com/papers/ijtsrd109.pdf

The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers.

Support Vector Machines, Genetic Algorithms, Neural networks, Classification, Kernel Function, Fitness, Cross-Validation


IJTSRD109
Volume-1 | Issue-4, June 2017
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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