Shape quantization and recognition with randomized trees
Neural Computation
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Machine Learning
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
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Local Binary Patterns for Human Detection on Hexagonal Structure
ISM '07 Proceedings of the Ninth IEEE International Symposium on Multimedia
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving object detection with boosted histograms
Image and Vision Computing
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A discriminative feature space for detecting and recognizing faces
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Human detection is a classical challenging problem in computer vision. To achieve satisfied performance in human detection, both suitable feature representation and effective detector (often classifier) are indispensable. In this paper, we propose a method of boosted forest with harr-like features for human detection. The proposed detection method associates the random decision trees as weak learners within the framework of Adaboost. Accordingly, these random trees are dynamically combined into a strong classifier, i.e., a boosted forest. The boosting process avoids the blindness and casualness of the tree selection in typical random forest algorithm. Besides, potent features are estimated and chosen in the process. Experiments on PASCAL VOC 2008 dataset demonstrate the effectiveness and efficiency of the proposed method.