Paper Info
Title
A 3d Feature-Based Binocular Tracking Algorithm
Abstract
This paper presents a 3D feature-based binocular tracking algorithm for tracking crowded people indoors. The algorithm consists of a two stage 3D feature points grouping method and a robust 3D feature-based tracking method. The two stage 3D feature points grouping method can use kernel-based ISODATA method to detect people accurately even though the part or almost full occlusion occurs among people in surveillance area. The robust 3D feature-based Tracking method combines interacting multiple model (IMM) method with a cascade multiple feature data association method. The robust 3D feature-based tracking method not only manages the generation and disappearance of a trajectory, but also can deal with the interaction of people and track people maneuvering. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.
Year
DOI
Venue
2006
10.1093/ietisy/e89-d.7.2142
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
stereo matching, dynamic cluster, human tracking, interacting multiple model, cascade multiple feature data association
Computer science,Robustness (computer science),Multispectral pattern recognition,Artificial intelligence,Trajectory,Kernel (linear algebra),Computer vision,Binocular vision,Pattern recognition,Algorithm,Feature extraction,Pattern matching,Feature data
Journal
Volume
Issue
ISSN
E89D
7
1745-1361
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Guang Tian121.42
Feihu Qi234727.19
Masatoshi Kimachi372.59
Y. Wu41178139.36
Takashi Iketani521.08