Contextual Priming for Object Detection
International Journal of Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
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
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'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
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We address the problem of pedestrian detection in still images. Many current pedestrian detection systems limit their performance by ignoring the underlying 3D geometric context in the image. We can estimate the geometric context by learning appearance-based method. We propose a novel context feature and local integrable features. These features are used for building many candidate weak classifiers by using linear SVM. Finally, MPL-Boost method selects the best weak classifiers suited for detection and construct the rejector-based cascade detector. We provide a thorough quantitative evaluation of our method on TUD-Brussels dataset and demonstrate that it outperforms the state-of-the-art pedestrian detector in recall rate, meanwhile, shows faster speed than other context incorporation method.