Boosting a weak learning algorithm by majority
Information and Computation
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Vector Boosting for Rotation Invariant Multi-View Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Joint Haar-like Features for Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automatic cascade training with perturbation bias
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Hi-index | 0.10 |
In this paper, we propose multi-view object detection methodology by using specific extended class of haar-like filters, which apparently detects the object with high accuracy in the unconstraint environments. There are several object detection techniques, which work well in restricted environments, where illumination is constant and the view angle of the object is restricted. The proposed object detection methodology successfully detects faces, cars, logo objects at any size and pose with high accuracy in real world conditions. To cope with angle variation, we propose a multiple trained cascades by using the proposed filters, which performs even better detection by spanning a different range of orientation in each cascade. We tested the proposed approach by still images by using image databases and conducted some evaluations by using video images from an IP camera placed in outdoor. We tested the method for detecting face, logo, and vehicle in different environments. The experimental results show that the proposed method yields higher classification performance than Viola and Jones's detector, which uses a single feature for each weak classifier. Given the less number of features, our detector detects any face, object, or vehicle in 15fps when using 4 megapixel images with 95% accuracy on an Intel i7 2.8GHz machine.