Proficient Use of Naïve Bayes Classifier in Object Tracking

  • Varsha S Futane University of Pune
  • Anilkumar N Holambe
Keywords: discriminative tracking, naive bayesian classifier, Gaussian function, object tracking, traind sample

Abstract

Real time object tracking is becoming a challenging ingredient in analysis of video imagery for efficient and robust object tracking. This work presents a tracking algorithm based on a set of naive Bayesian classifiers. We consider tracking as a classification problem and train online a set of classifiers which distinguish a target object from the background around it. This paper focuses naïve Bayes classifier approach for tracking a target object in a real-time video dataset. In equivalence to the still images, video sequences render more information on how objects and their scenarios vary overtime. It is always an ambitious task in order to formulate an efficient appearance model. Imprecise extraction of target object and background in model adaptation causes a serious drift problem which leads in degradation of tracking performance. During Pre-processing stages, challenges like illumination, pose variation, occlusion are to be looked upon. This problem can be overcome by continuous detection approach of the target object in each frame. In this approach we formulate a binary classification with the help of a naive Bayes classifier in a compressed knowledge base(domain).

References

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Published
2018-03-23
How to Cite
Futane, V., & Holambe, A. (2018). Proficient Use of Naïve Bayes Classifier in Object Tracking. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 1(1). Retrieved from http://www.asianssr.org/index.php/ajct/article/view/119
Issue
Vol 1 (2015): Issue VI
Section
Article

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