A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Simultaneous Estimation of Segmentation and Shape
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast Asymmetric Learning for Cascade Face Detection
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
| Hi-index | 0.00 |
Boosted cascade proposed by Viola and Jones is applied to many object detection problems. In their cascade, the confidence value of each stage can only be used in the current stage so that interstage information is not utilized to enhance classification performance. In this paper, we present a new cascading structure added SVM stages which employ the confidence values of multiple preceding Adaboost stages as input. Specifically, a rejection hyperplane and a promotion hyperplane are learned for each added SVM stage. During detection process, negative detection windows are discarded earier by the rejection SVM hyperplane, and positive windows with high confidence value are boosted by promotion hyperplane to bypass the next stage of cascade. In order to construct the two distinct hyperplanes, different cost coefficients for training samples are chosen in SVM learning. Experiment results in UIUC data set demonstrate that the proposed method achieve high detection accuracy and better efficiency.