Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking

Applications for real-time visual tracking can be found in many areas, including visual odometry and augmented reality. Interest point detection and feature description form the basis of feature-based tracking, and a variety of algorithms for these tasks have been proposed. In this work, we present...

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Bibliographic Details
Published in:International journal of computer vision 2011-09, Vol.94 (3), p.335-360
Main Authors: Gauglitz, Steffen, Höllerer, Tobias, Turk, Matthew
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Applied sciences
Artificial Intelligence
Augmented reality
Cameras
Computer Imaging
Computer Science
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Datasets
Detectors
Exact sciences and technology
Image Processing and Computer Vision
Lighting
Pattern Recognition
Pattern Recognition and Graphics
Pattern recognition. Digital image processing. Computational geometry
Sensors
Software
Surface layer
Tasks
Texture
Tracking
Vision
Visual
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Summary:Applications for real-time visual tracking can be found in many areas, including visual odometry and augmented reality. Interest point detection and feature description form the basis of feature-based tracking, and a variety of algorithms for these tasks have been proposed. In this work, we present (1) a carefully designed dataset of video sequences of planar textures with ground truth, which includes various geometric changes, lighting conditions, and levels of motion blur, and which may serve as a testbed for a variety of tracking-related problems, and (2) a comprehensive quantitative evaluation of detector-descriptor-based visual camera tracking based on this testbed. We evaluate the impact of individual algorithm parameters, compare algorithms for both detection and description in isolation, as well as all detector-descriptor combinations as a tracking solution. In contrast to existing evaluations, which aim at different tasks such as object recognition and have limited validity for visual tracking, our evaluation is geared towards this application in all relevant factors (performance measures, testbed, candidate algorithms). To our knowledge, this is the first work that comprehensively compares these algorithms in this context, and in particular, on video streams.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-011-0431-5