Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Coarse-to-Fine Support Vector Classifiers for Face Detection
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robust pedestrian detection and tracking in crowded scenes
Image and Vision Computing
Object detection using spatial histogram features
Image and Vision Computing
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-centric evaluation for efficient cascaded object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
Different classifiers show different sensitivities to translation-variance. The translation-insensitive classifiers are capable of accelerating the detection process by searching over a coarse grid as well as guaranteeing the recall rate. In this paper, we define a concept of Translation-Tolerable Region (TTR) for a classifier. The TTR is such a region that all the detection windows in it have consistent (stable) results output by the classifier. We use the classifier's Maximal Translation-Tolerable Region (MTTR) to measure its sensitivity to the translation-variance. For object detection, we propose an algorithm for training the discriminative classifiers as well as learning the associated MTTRs. The discriminative classifiers are assembled into a cascaded classifier in descending order of their MTTR sizes. To speed up the detection process, we propose a Granularity-Adaptively-Tunable (GAT) search strategy according to the classifiers' MTTRs. Furthermore, we prove that the recall rate is Probably Approximately Admissible (PAA) in the GAT search, which means that the proposed approach can theoretically guarantee the accuracy while accelerating the detection process. Based on the boosting framework with Histograms of Oriented Gradients (HOG) features, we evaluate the proposed approach on the public datasets containing both rigid and non-rigid object classes. The experimental results show that our approach achieves considerable results with a fast speed.