A hierarchical clustering based non-maximum suppression method in pedestrian detection

Authors:
Bing Shuai;Yun Cheng;Shaozi Li;Songzhi Su
Affiliations:
Department of Cognitive Science, Xiamen University, China,Fujian Key Lab of Brain-Like Intelligent System, Xiamen, Fujian, China;Department of Communication and Control Engineering, Hunan Institute of Humanities, Science and Technology, Loudi, P.R. China;Department of Cognitive Science, Xiamen University, China,Fujian Key Lab of Brain-Like Intelligent System, Xiamen, Fujian, China;School of Information Science and Technology, Xiamen University, China,Fujian Key Lab of Brain-Like Intelligent System, Xiamen, Fujian, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

Citing 8
Cited 0

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
Robust Real-Time Face Detection

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
Survey of Pedestrian Detection for Advanced Driver Assistance Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast dual method for HIK SVM learning

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Disparity statistics for pedestrian detection: combining appearance, motion and stereo

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
The estimation of the gradient of a density function, with applications in pattern recognition

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We learned that one true positive would have a cluster with dense detected windows near the geometric center of pedestrian, so we adopted clustering methods based on ellipse Euclidean distance to get the location of pedestrian. Moreover, considering the big-size pedestrians and small ones respond differently to the same classifier and a ‘weak' true positive (few fire times) may be filtered, we partitioned the non-maximum suppression process into two parts to analyze them distinctively. We call this method hierarchical non-maximum suppression. The experiment showed that our non-hierarchical clustering based method did well as proposed by Dalal and consumed much less time (nearly 100 fold less time at 150 magnitude windows), while the proposed hierarchical algorithm recalled more true positives than the non-hierarchical method (5% percent higher detection rate at FPPI = 1).