Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2009-10-07
IEEE Symposium on Intelligent Vehicles (IV'2009), XiAn : China (2009)
Computer Science
Computer Vision and Pattern Recognition
Scientific paper
We present promising results for visual object categorization, obtained with adaBoost using new original ?keypoints-based features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (a kind of 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. Preliminary tests on a small subset of a pedestrians database also gives promising 97% recall with 92 % precision, which shows the generality of our new family of features. 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? in the case of lateral-cars) and thus have a ?semantic? meaning. We also made a first test on video for detecting vehicles from adaBoostselected keypoints filtered in real-time from all detected keypoints.
Bdiri Taoufik
Moutarde Fabien
Steux Bruno
No associations
LandOfFree
Visual object categorization with new keypoint-based adaBoost features does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Visual object categorization with new keypoint-based adaBoost features, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Visual object categorization with new keypoint-based adaBoost features will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-36634