Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
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We present a new method for object detection that integrates part-based model with cascades of boosted classifiers. The parts are labeled in a supervised manner. For each part, we construct a boosted cascade by selecting the most important features from a large set and combining more complex classifiers. The weak learners used in each level of the cascade are gradient features of variable-size blocks. Moreover, we learn a model of the spatial relations between those parts. In detection, the cascade of classifiers for each part compute the part values within all sliding windows and then the object is localized within the image by integrating the spatial relations model. The experimental results demonstrate that training a cascade of boosted classifiers for each part and adding spatial constraints among parts improve performance of detection and localization.