Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 94, Issue 3, Pages 335-360Publisher
SPRINGER
DOI: 10.1007/s11263-011-0431-5
Keywords
Interest point detectors; Feature descriptors; Visual tracking; Dataset; Evaluation
Categories
Funding
- UCSB, NSF [IIS-0747520]
- ONR [N00014-09-1-1113]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0747520] Funding Source: National Science Foundation
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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.
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