Adaboost with "Keypoint Presence Features" for Real-Time Vehicle Visual Detection
Abstract
We present promising results for real-time vehicle visual detection, obtained with adaBoost using new original ?keypoints presence features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (~ a SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as ?wheel? or ?side skirt?) and thus have a ?semantic? meaning.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2009
- DOI:
- 10.48550/arXiv.0910.1273
- arXiv:
- arXiv:0910.1273
- Bibcode:
- 2009arXiv0910.1273B
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning
- E-Print:
- 16th World Congress on Intelligent Transport Systems (ITSwc'2009), Su\`ede (2009)