Object Tracking Using Features Extracted From Compressed Domain

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-30 Number-2
Year of Publication : 2015
Authors : Miss. Pratiksha R.Bhalekar, Ms. Vaishali Suryawanshi
DOI :  10.14445/22312803/IJCTT-V30P113


Miss. Pratiksha R.Bhalekar, Ms. Vaishali Suryawanshi "Object Tracking Using Features Extracted From Compressed Domain". International Journal of Computer Trends and Technology (IJCTT) V30(2):75-80, December 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
It is a challenging task to develop effective and efficient models for robust object tracking due to factors such as pose variation, illumination changes, occlusion, and movement obscure. Our methodology is Object tracking using features extracted from Compressed Domain. Features are extracted from the compressed domain with a Discrete Cosine Transform. We pack test of pictures of the frontal range target and the establishment using the same Discrete Cosine Transform. The system can be considered as generative because the target can be well represented theoretically with the features generated randomly. It is additionally discriminative since it utilizes these features to discrete the objective from the encompassing foundation. Calculating similarity measure utilizing Euclidian distance. Position tracking after is similarly done using Euclidian separation. These tracking results are compared with mean shift tracking.

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Object Tracking, Discrete Cosine Transform (DCT), Background Subtraction Method, Euclidian distance, Scale Invariant Feature Transform (SIFT)