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
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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 Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Yet Even Faster (YEF) real-time object detection
International Journal of Intelligent Systems Technologies and Applications
Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
Proceedings of the 30th DAGM symposium on Pattern Recognition
High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
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
A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle
International Journal of Robotics Research
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Category independent object proposals
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multi-stage sampling with boosting cascades for pedestrian detection in images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Recursive coarse-to-fine localization for fast object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
Transferring Boosted Detectors Towards Viewpoint and Scene Adaptiveness
IEEE Transactions on Image Processing
Assemble New Object Detector With Few Examples
IEEE Transactions on Image Processing
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Due to the large amount of data to be processed by visual applications aiming at extracting high-level understanding of the scene, low-level methods such as object detection are required to have not only high accuracy but also low computational cost in order to provide fast and reliable information. Training sets containing samples representing multiple scenes are used to learn object detectors that can be reliably used in different scenarios. In general, information extracted from multiple feature channels is combined to capture the large variability present in these different environments. Although this approach provides accurate detection results, it usually leads to a high computational cost. On the other hand, if characteristics of the scene are known before-hand, a set of simple and fast computing features might be sufficient to provide high accuracy at a low computational cost. Therefore, it is valuable to seek a balance between these two extremes such that the detection method not only works well in different scenarios but also is able to extract enough information from a scene. We integrate a set of data-driven regression models with a multi-stage based human detection method trained to be used in different environments. The regressions are used to estimate the detector response at each stage and the location of the objects. The use of the regression models allows the method to reject large number of detection windows quickly. Experimental results based on human detection show that the addition of the regression models reduces the computational cost by as much as ten times with very small or no degradation on detection accuracy.