Robust Real-Time Face Detection
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
WaldBoost " Learning for Time Constrained Sequential Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
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
Tracking by an Optimal Sequence of Linear Predictors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Deformable Detector
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated window. The classification process is sequentially interleaved with estimating the correct position. The position estimate is used for steering the features yet to be evaluated. This locally interleaved sequential alignment (LISA) allows to run an object detector on sparser grid which speeds up the process. The position alignment is jointly learned with the detector. We achieve a better detection rate since the method allows for training the detector on perfectly aligned image samples. For estimation of the alignment we propose a learnable regressor that approximates a non-linear regression function and runs in negligible time.