| Records |
| Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
| Title |
Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection |
Type |
Conference Article |
| Year |
2013 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages  |
467 - 472 |
| Keywords |
Pedestrian Detection; Virtual World; Part based |
| Abstract |
State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). |
| Address |
Gold Coast; Australia; June 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
Medium |
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| Area |
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Expedition |
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Conference |
IV |
| Notes |
ADAS; 600.054; 600.057 |
Approved |
no |
| Call Number |
XVL2013; ADAS @ adas @ xvl2013a |
Serial |
2214 |
| Permanent link to this record |
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| Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
| Title |
Learning a Part-based Pedestrian Detector in Virtual World |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
15 |
Issue |
5 |
Pages |
2121-2131 |
| Keywords |
Domain Adaptation; Pedestrian Detection; Virtual Worlds |
| Abstract |
Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM. |
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Place of Publication |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ XVL2014 |
Serial |
2433 |
| Permanent link to this record |
