Abstract
We propose a novel method for visual place recognition using bag of words obtained from accelerated segment test (FAST)+BRIEF features. For the first time, we build a vocabulary tree that discretizes a binary descriptor space and use the tree to speed up correspondences for geometrical verification. We present competitive results with no false positives in very different datasets, using exactly the same vocabulary and settings. The whole technique, including feature extraction, requires 22 ms/frame in a sequence with 26 300 images that is one order of magnitude faster than previous approaches.
Keywords
Artificial intelligenceComputer scienceVocabularyPattern recognition (psychology)Feature extractionBinary treeFalse positive paradoxBinary numberFrame (networking)Tree (set theory)Feature (linguistics)Sequence (biology)Bag-of-words model in computer visionImage (mathematics)Binary imageIdentification (biology)Computer visionVisual WordImage processingMathematicsImage retrievalAlgorithmArithmetic
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Publication Info
- Year
- 2012
- Type
- article
- Volume
- 28
- Issue
- 5
- Pages
- 1188-1197
- Citations
- 1823
- Access
- Closed
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Cite This
Dorian Gálvez‐López,
Juan D. Tardós
(2012).
Bags of Binary Words for Fast Place Recognition in Image Sequences.
IEEE Transactions on Robotics
, 28
(5)
, 1188-1197.
https://doi.org/10.1109/tro.2012.2197158
Identifiers
- DOI
- 10.1109/tro.2012.2197158