Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Shape Localization Based on Statistical Method Using Extended Local Binary Pattern
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
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
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
A Performance Evaluation of Single and Multi-feature People Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
A Pose-Invariant Descriptor for Human Detection and Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
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This paper addresses the problem of human detection in still images. We first describe a novel descriptor concatenating the local normalized histogram of oriented gradients (HOG) and the global normalized histogram of census transform (CT) of images for human detection. The detector is trained by using cascade learning method based on AdaBoost. In addition, we propose an easy histogram-based search method, termed the block histogram, which can reduce the computational cost and speed up the process of detection when sliding in the test image. Experimental results on the INRIA person dataset show that the proposed method can achieve competitive results both in discriminating power and detection speed as compared to the state-of-the-art.