Name
Abstract | ||
|---|---|---|
This paper introduces a part-based two-stage pedestrian detector. The system finds pedestrian candidates with an AdaBoost cascade on Haar-like features. It then verifies each candidate using a part-based HOG-SVM doing first a regression and then a classification based on the estimated function output from the regression. It uses the Histogram of Oriented Gradients (HOG) computed on both the full, upper and lower body of the candidates, and uses these in the final verification. The system has been trained and tested on the INRIA dataset and performs better than similar previous work, which uses full-body verification. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ITSC.2012.6338898 | ITSC |
Keywords | Field | DocType |
feature extraction,gradient methods,learning (artificial intelligence),object detection,pedestrians,regression analysis,support vector machines,adaboost cascade,hog,haar-like features,inria dataset,estimated function output,full-body verification,histogram of oriented gradients,part-based hog-svm,regression,two-stage part-based pedestrian detection | Object detection,Computer vision,AdaBoost,Pattern recognition,Regression,Regression analysis,Computer science,Support vector machine,Feature extraction,Histogram of oriented gradients,Artificial intelligence,Pedestrian detection | Conference |
ISSN | ISBN | Citations |
2153-0009 E-ISBN : 978-1-4673-3062-6 | 978-1-4673-3062-6 | 9 |
PageRank | References | Authors |
0.54 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Mogelmose | 1 | 138 | 8.98 |
| antonio prioletti | 2 | 34 | 2.55 |
| Mohan M. Trivedi | 3 | 6564 | 475.50 |
| Alberto Broggi | 4 | 1527 | 178.28 |
| Thomas Moeslund | 5 | 2721 | 186.08 |