The nature of statistical learning theory
The nature of statistical learning theory
Human motion analysis: a review
Computer Vision and Image Understanding
Topographic Maps and Local Contrast Changes in Natural Images
International Journal of Computer Vision
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Computer Vision and Applications: Volume 2: From Images to Features
Handbook of Computer Vision and Applications: Volume 2: From Images to Features
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Extracting Meaningful Curves from Images
Journal of Mathematical Imaging and 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
An a contrario Decision Framework for Region-Based Motion Detection
International Journal of Computer Vision
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
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Recognition of human behavior by space-time silhouette characterization
Pattern Recognition Letters
A color topographic map based on the dichromatic reflectance model
Journal on Image and Video Processing - Color in Image and Video Processing
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to urban pedestrian detection for automatic braking
IEEE Transactions on Intelligent Transportation Systems
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cascaded confidence filtering for improved tracking-by-detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
3D texton spaces for color-texture retrieval
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-class boosting with asymmetric binary weak-learners
Pattern Recognition
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This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10^-^1 false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6fps on 640x480 pixel captures. This is therefore a fast and reliable pedestrian detector.