W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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 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
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Description of interest regions with local binary patterns
Pattern Recognition
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition based on combination of human perception and local binary pattern
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Noise robust rotation invariant features for texture classification
Pattern Recognition
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Detecting pedestrians in images and videos plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we presents a novel feature, termed pyramid center-symmetric local binary/ternary patterns (pyramid CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al. [1] mainly captures the texture information. The proposed CS-LBP feature, in contrast, captures the gradient information. Moreover, the pyramid CS-LBP/LTP is easy to implement and computationally efficient, which is desirable for realtime applications. Experiments on the INRIA pedestrian dataset show that the proposed feature outperforms the histograms of oriented gradients (HOG) feature and comparable with the start-of-the-art pyramid HOG (PHOG) feature when using the intersection kernel support vector machines (HIKSVMs). We also demonstrate that the combination of our pyramid CS-LBP feature and the PHOG feature could significantly improve the detection performance--producing state-of-the-art accuracy on the INRIA pedestrian dataset.