Object Tracking using Multi Adaptive Feature Extraction Technique

Object Tracking using Multi Adaptive Feature Extraction Technique
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© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Sreehari Patibandla, Maruthavanan Archana, Rama Chaithanya Tanguturi
DOI : 10.14445/22315381/IJETT-V70I6P229

How to Cite?

Sreehari Patibandla, Maruthavanan Archana, Rama Chaithanya Tanguturi, "Object Tracking using Multi Adaptive Feature Extraction Technique," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 279-286, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P229

Abstract
Object tracking is a complex issue in computer vision; despite fast motion and motion blur, most relevant techniques have produced lesser performances. To overcome the complex issues of feature extraction, the enhanced technology of the Multi Adaptive Feature Extraction (MAFE) technique is proposed in this paper that has identified the sub-template feature for the object tracking process. The similarity values of the sub-template feature have been computed through the histogram, and they can be identified with the similarity matrix function. The classification process is enabled with Jeffrey's entropy that the updated sub-template. The performance evaluation involves the VTB dataset for evaluating the performance of the proposed MAFE technique, which is compared with the related techniques. The result proves that the proposed methodology has an improved success rate than other techniques.

Keywords
Object tracking, Multi Adaptive Feature Extraction, Jeffrey's entropy, success rate, the posterior probability.

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