Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
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
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Improving object detection with boosted histograms
Image and Vision Computing
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
A hierarchical image kernel with application to pedestrian identification for video surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color contribution to part-based person detection in different types of scenarios
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Boosted local structured HOG-LBP for object localization
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
A Multilevel Mixture-of-Experts Framework for Pedestrian Classification
IEEE Transactions on Image Processing
Segmentation as selective search for object recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.00 |
In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement. We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4.47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.