CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
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
Quasi-Random Sampling for Condensation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
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
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Putting Objects in Perspective
International Journal of Computer Vision
Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
Proceedings of the 30th DAGM symposium on Pattern Recognition
A Performance Evaluation of Single and Multi-feature People Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Multiperson Tracking from a Mobile Platform
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
Crosstalk cascades for frame-rate pedestrian detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A data-driven detection optimization framework
Neurocomputing
Local context priors for object proposal generation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Many works address the problem of object detection by means of machine learning with boosted classifiers. They exploit sliding window search, spanning the whole image: the patches, at all possible positions and sizes, are sent to the classifier. Several methods have been proposed to speed up the search (adding complementary features or using specialized hardware). In this paper we propose a statistical-based search approach for object detection which uses a Monte Carlo sampling approach for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multi-stage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifier (i.e. its response). For videos, this approach is plugged in a Bayesian-recursive framework which exploits the temporal coherency of the pedestrians. Several tests on both still images and videos on common datasets are provided in order to demonstrate the relevant speedup and the increased localization accuracy with respect to sliding window strategy using a pedestrian classifier based on covariance descriptors and a cascade of Logitboost classifiers.