Paper Info
Title
A robust 3d feature-based people stereo tracking algorithm
Abstract
This paper presents a 3D feature-based people tracking algorithm which combines an interacting multiple model (IMM) algorithm with a cascade multiple feature data association algorithm. The IMM algorithm in this paper only uses an adaptive Kalman Filter and two dynamic models consisting of a constant velocity model (CV) and a current statistics model (CS) to predict the 3D location of people maneuvering and update the prediction with corresponding measurement. The cascade multiple feature data association algorithm in this paper utilizes three hypotheses, including the nearest distance hypothesis, the velocity consistency hypothesis, and the intensity consistency hypothesis, in turn to determine which trajectory a measurement should be assigned to. 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
2005
10.1007/11538356_91
ICIC (2)
Keywords
Field
DocType
dynamic model,stereo tracking algorithm,association algorithm,people maneuvering,current statistics model,constant velocity model,cascade multiple feature data,interacting multiple model,intensity consistency hypothesis,imm algorithm,feature-based people,statistical model,kalman filter
Pattern recognition,Stereopsis,Computer science,Algorithm,Kalman filter,Robustness (computer science),Artificial intelligence,Adaptive filter,Acceleration,Cascade,Trajectory,Feature data
Conference
Volume
ISSN
ISBN
3645
0302-9743
3-540-28227-0
Citations 
PageRank 
References 
2
0.40
4
Authors
8
Name
Order
Citations
PageRank
Guang Tian121.42
Feihu Qi234727.19
Yong Fang320.40
Masatoshi Kimachi472.59
Y. Wu51178139.36
Takashi Iketani621.08
Xin Mao793.99
Panjun Chen820.40