CAMSHIFT IMPROVEMENT WITH MEAN-SHIFT SEGMENTATION, REGION GROWING, AND SURF METHOD

Ferdinan, Ferdinan and Suryana, Yaya (2013) CAMSHIFT IMPROVEMENT WITH MEAN-SHIFT SEGMENTATION, REGION GROWING, AND SURF METHOD. Jurnal CommIT, 07 (02). ISSN 1979-2484

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Abstract

Abstract: CAMSHIFT algorithm has been widely used in object tracking. CAMSHIFT utilizes color features as the model object. Thus, original CAMSHIFT may fail when the object color is similar with the background color. In this study, we propose CAMSHIFT tracker combined with mean-shift segmentation, region growing, and SURF in order to improve the tracking accuracy. The mean-shift segmentation and region growing are applied in object localization phase to extract the important parts of the object. Hue-distance, saturation, and value are used to calculate the Bhattacharyya distance to judge whether the tracked object is lost. Once the object is judged lost, SURF is used to find the lost object, and CAMSHIFT can retrack the object. The Object tracking system is built with OpenCV. Some measurements of accuracy have done using frame-based metrics. We use datasets BoBoT (Bonn Benchmark on Tracking) to measure accuracy of the system. The results demonstrate that CAMSHIFT combined with mean-shift segmentation, region growing, and SURF method has higher accuracy than the previous methods.

Item Type: Article
Additional Information: 34_Volume 07 / Nomor 02 / October 2013_CAMSHIFT IMPROVEMENT WITH MEAN-SHIFT SEGMENTATION, REGION GROWING, AND SURF METHOD
Subjects: ?? subjectjournal_34 ??
Divisions: ?? journal_30_34_Volume-07-Nomor-02-October-2013 ??
Depositing User: Mr. Super Admin
Date Deposited: 27 Apr 2015 18:23
Last Modified: 27 Apr 2015 18:23
URI: http://eprints.binus.ac.id/id/eprint/31410

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