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Proceedings Paper

Object tracking on mobile devices using binary descriptors
Author(s): Andreas Savakis; Mohammad Faiz Quraishi; Breton Minnehan
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Paper Abstract

With the growing ubiquity of mobile devices, advanced applications are relying on computer vision techniques to provide novel experiences for users. Currently, few tracking approaches take into consideration the resource constraints on mobile devices. Designing efficient tracking algorithms and optimizing performance for mobile devices can result in better and more efficient tracking for applications, such as augmented reality. In this paper, we use binary descriptors, including Fast Retina Keypoint (FREAK), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Independent Features (BRIEF), and Binary Robust Invariant Scalable Keypoints (BRISK) to obtain real time tracking performance on mobile devices. We consider both Google’s Android and Apple’s iOS operating systems to implement our tracking approach. The Android implementation is done using Android’s Native Development Kit (NDK), which gives the performance benefits of using native code as well as access to legacy libraries. The iOS implementation was created using both the native Objective-C and the C++ programing languages. We also introduce simplified versions of the BRIEF and BRISK descriptors that improve processing speed without compromising tracking accuracy.

Paper Details

Date Published: 6 March 2015
PDF: 8 pages
Proc. SPIE 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015, 94080B (6 March 2015); doi: 10.1117/12.2080786
Show Author Affiliations
Andreas Savakis, Rochester Institute of Technology (United States)
Mohammad Faiz Quraishi, Rochester Institute of Technology (United States)
Breton Minnehan, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9408:
Imaging and Multimedia Analytics in a Web and Mobile World 2015
Qian Lin; Jan P. Allebach; Zhigang Fan, Editor(s)

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