Machine Learning
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
Example-Based Object Detection in Images by Components
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
Distinctive Image Features from Scale-Invariant Keypoints
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Cascade Classifier Using Divided CoHOG Features for Rapid Pedestrian Detection
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Object classification using heterogeneous co-occurrence features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Object classification using heterogeneous co-occurrence features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Face recognition using Histograms of Oriented Gradients
Pattern Recognition Letters
Fast and accurate pedestrian detection using a cascade of multiple features
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
A new pedestrian detection descriptor based on the use of spatial recurrences
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Pedestrian recognition using second-order HOG feature
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Spatial feature interdependence matrix (SFIM): a robust descriptor for face recognition
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Feature extraction based on co-occurrence of adjacent local binary patterns
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Improving HOG with image segmentation: application to human detection
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Semantic interpretation of novelty in images using histograms of oriented gradients
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Spatial Recurrences for Pedestrian Classification
Journal of Mathematical Imaging and Vision
Object class detection: A survey
ACM Computing Surveys (CSUR)
Pedestrian detection based on kernel discriminative sparse representation
Transactions on Edutainment IX
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The purpose of this paper is to detect pedestrians from images. This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG). Including co-occurrence with various positional offsets, the feature descriptors can express complex shapes of objects with local and global distributions of gradient orientations. Our method is evaluated with a simple linear classifier on two famous pedestrian detection benchmark datasets: "DaimlerChrysler pedestrian classification benchmark dataset " and "INRIA person data set ". The results show that proposed method reduces miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets.