Paper Info
Title
Segmentation of Pedestrians with Confidence Level Computation
Abstract
In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism.
Year
DOI
Venue
2013
10.1007/s11265-012-0708-0
Signal Processing Systems
Keywords
Field
DocType
Pedestrian segmentation,Intelligent surveillance,Human detection,Curvelet,Histogram of oriented gradients,Modified Haar of oriented gradients
Computer vision,Pedestrian,Segmentation,Computer science,Histogram of oriented gradients,Artificial intelligence,Cluster analysis,Confidence interval,Detector,Machine learning,Curvelet,Computation
Journal
Volume
Issue
ISSN
72
2
1939-8018
Citations 
PageRank 
References 
2
0.38
30
Authors
4
Name
Order
Citations
PageRank
Hsu-Yung Cheng124323.56
You-Jhen Zeng220.38
Chien-cheng Lee317415.60
Shih-Han Hsu4211.44