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VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Acharya, Aniruddha K and Babu, Venkatesh R (2014) VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 04-09, 2014, Florence, ITALY.

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Official URL: http://dx.doi.org/ 10.1109/ICASSP.2014.6854863

Abstract

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

Item Type: Conference Proceedings
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Additional Information: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, ITALY, MAY 04-09, 2014
Keywords: Object tracking; Classification; Random Ferns
Department/Centre: Division of Information Sciences > Supercomputer Education & Research Centre
Date Deposited: 12 Jan 2015 06:50
Last Modified: 12 Jan 2015 06:50
URI: http://eprints.iisc.ernet.in/id/eprint/50604

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