SUSAN—A New Approach to Low Level Image Processing
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
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Robust Texture Classification by Subsets of Local Binary Patterns
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Two-Stage Template Approach to Person Detection in Thermal Imagery
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'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
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
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Pedestrian detection by means of far-infrared stereo vision
Computer Vision and Image Understanding
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Description of interest regions with local binary patterns
Pattern Recognition
Contour-motion feature (CMF): A space-time approach for robust pedestrian detection
Pattern Recognition Letters
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
Hi-index | 0.02 |
This paper presents a robust real-time pedestrian detection approach from infrared (IR) videos using binary pattern features. A novel pyramid binary pattern (PBP) feature is first proposed for IR pedestrian appearance representation. Both symmetry and spatial layout of texture cells have been encapsulated in the PBP feature. PBP outperforms several state-of-the-art binary pattern features for IR pedestrian images classification. Motivated by the recent success of motion-enhanced pedestrian detector, we then extend the PBP feature to 3D spatial-temporal volumes. The dynamic PBP feature combines both motion and appearance for IR pedestrian description and achieves better performance in comparison to the static PBP feature. Finally, a keypoint based sliding window support vector machine (SVM) classifier is used to detect pedestrians in IR videos. The keypoint based scanning strategy reduces the number of candidate sub-windows dramatically. The proposed approach has been implemented on an experimental vehicle equipped with a forward-looking infrared (FLIR) camera. Experimental results in various urban scenarios demonstrate the effectiveness and robustness of our approach. In addition, even though our approach is presented for IR imageries, it can also be applied to pedestrian detection in visual images.