Name
Abstract | ||
|---|---|---|
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 adaBoost-selected keypoints filtered in real-time from all detected keypoints. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IVS.2009.5164310 | Computing Research Repository |
Keywords | DocType | Volume |
automobiles,image classification,learning (artificial intelligence),object detection,traffic engineering computing,video signal processing,boolean response,keypoint-based adaboost features,lateral-viewed cars,pedestrian database,vehicle video detection,visual object categorization,pattern recognition,real time | Journal | abs/0910.1 |
ISSN | ISBN | Citations |
1931-0587 E-ISBN : 978-1-4244-3504-3 | 978-1-4244-3504-3 | 1 |
PageRank | References | Authors |
0.40 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taoufik Bdiri | 1 | 77 | 5.26 |
| Fabien Moutarde | 2 | 54 | 15.26 |
| Bruno Steux | 3 | 39 | 5.28 |