Human detection based on pyramidal statistics of oriented filtering and online learned scene geometrical model

Authors:
Min Li;Qi Hu;Yu Wang;Weishan Dong
Affiliations:
IBM China Research Laboratory, Diamond A, Zhongguancun Software Park, Haidian District, Beijing 100193, China;IBM China Research Laboratory, Diamond A, Zhongguancun Software Park, Haidian District, Beijing 100193, China;IBM China Research Laboratory, Diamond A, Zhongguancun Software Park, Haidian District, Beijing 100193, China;IBM China Research Laboratory, Diamond A, Zhongguancun Software Park, Haidian District, Beijing 100193, China
Venue:
Neurocomputing
Year:
2013

Citing 19
Cited 1

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Introduction to algorithms

Introduction to algorithms
Probabilistic Methods for Finding People

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance

International Journal of Computer Vision
Pedestrian Detection in Crowded Scenes

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'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
Object Recognition with Features Inspired by Visual Cortex

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Incremental Online Learning in High Dimensions

Neural Computation
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
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields

International Journal of Computer Vision
Rapid and robust human detection and tracking based on omega-shape features

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Region covariance: a fast descriptor for detection and classification

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Overview and recent advances in partial least squares

SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

IEEE Transactions on Image Processing

Regressing Local to Global Shape Properties for Online Segmentation and Tracking

International Journal of Computer Vision

Quantified Score

Hi-index	0.01

Visualization

Abstract

We study the problem of robust human detection. In this paper, a new descriptor, Pyramidal Statistics of Oriented Filtering (PSOF), is proposed for human shape representation. Unlike traditional one-scale gradient-based methods, the PSOF descriptor utilizes a Gabor filter bank to obtain multi-scale pixel-level orientation information and makes use of locally normalized pyramidal statistics of these Gabor responses to represent object shape, which shows great robustness to image noise and blur. Besides, to exclude detection outliers that violate perspective projection in image sequence, a geometrical model is learned online to describe the relationship between object's average height and the foot-point coordinate. Experimental results on both static images and video sequences show that PSOF detector performs much better than one of the state-of-the-art detectors.