This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Integrated target tracking and recognition via joint appearance-motion generative models
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Vijay Venkataraman, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, 74078 USA
Xin Fan, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, 74078 USA
Guoliang Fan, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, 74078 USA
We address the problem of joint target tracking and recognition by incorporating both appearance and motion information via two generative models. Specifically, a non-linear tensor decomposition method is used to develop an appearance generative model for multi-pose target representation. In addition, a target-dependent kinematic model is invoked to capture different target dynamics. Both generative models are integrated in a graphical model to work together for joint estimation of the kinematics, pose, and identity of the target. A particle filter is developed for inference in the graphical model where a Kalman filter is embedded to improve the proposal generation by taking advantage of motion cues. Tests on simulated infrared sequences demonstrate the advantages and potential of the proposed approach for joint tracking and recognition.
Citation:
Vijay Venkataraman, Xin Fan, Guoliang Fan, "Integrated target tracking and recognition via joint appearance-motion generative models," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
Usage of this product signifies your acceptance of the Terms of Use.