The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Pedestrian Detection via Classification on Riemannian Manifolds
Open accessAbstract
We present a new algorithm to detect pedestrian in still images utilizing covariance matrices as object descriptors. Since the descriptors do not form a vector space, well known machine learning techniques are not well suited to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. The main contribution of the paper is a novel approach for classifying points lying on a connected Riemannian manifold using the geometry of the space. The algorithm is tested on INRIA and DaimlerChrysler pedestrian datasets where superior detection rates are observed over the previous approaches.
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Cites
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Histograms of Oriented Gradients for Human Detection
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Cited by
Pedestrian detection: A benchmark
Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206631 · Cited by 1,313
Multiclass Brain–Computer Interface Classification by Riemannian Geometry
Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172210 · Cited by 878
Classification of covariance matrices using a Riemannian-based kernel for BCI applications
Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.12.039 · Cited by 493
Semantic Segmentation with Second-Order Pooling
Carreira, J., Caseiro, R., Batista, J., & Sminchisescu, C. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33786-4_32 · Cited by 458
Filtered channel features for pedestrian detection
Zhang, S., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298784 · Cited by 395
Metadata
- Authors
- Tuzel, O., Porikli, F., & Meer, P.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-10
- Volume
- 30
- Issue
- 10
- Pages
- 1713-1727
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2116022929
- Cited by
- 975
- References
- 9
- FWCI
- 78.28
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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