TY - GEN
T1 - An online motion-based particle filter for head tracking applications
AU - Bouaynaya, Nidhal
AU - Qu, Wei
AU - Schonfeld, Dan
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - Particle filtering framework has revolutionized probabilistic tracking of objects in a video sequence. In this framework the proposal density can be any density as long as its support includes that of the posterior. However, in practice, the number of samples is finite and consequently the choice of the proposal is crucial to the effectiveness of the tracking. The CONDENSATION filter uses the transition prior as the proposal density. We propose in this paper a motion-based proposal. We use Adaptive Block Matching (ABM) as the motion estimation technique. The benefits of this model are two fold. It increases the sampling efficiency and handles abrupt motion changes. Analytically, we derive a Kullback-Leibler (KL)-based performance measure and show that the motion proposal is superior to the proposal of the CONDENSATION filter. Our experiments are applied to head tracking. Finally, we report promising tracking results in complex environments.
AB - Particle filtering framework has revolutionized probabilistic tracking of objects in a video sequence. In this framework the proposal density can be any density as long as its support includes that of the posterior. However, in practice, the number of samples is finite and consequently the choice of the proposal is crucial to the effectiveness of the tracking. The CONDENSATION filter uses the transition prior as the proposal density. We propose in this paper a motion-based proposal. We use Adaptive Block Matching (ABM) as the motion estimation technique. The benefits of this model are two fold. It increases the sampling efficiency and handles abrupt motion changes. Analytically, we derive a Kullback-Leibler (KL)-based performance measure and show that the motion proposal is superior to the proposal of the CONDENSATION filter. Our experiments are applied to head tracking. Finally, we report promising tracking results in complex environments.
UR - http://www.scopus.com/inward/record.url?scp=33646785076&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646785076&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1415382
DO - 10.1109/ICASSP.2005.1415382
M3 - Conference contribution
AN - SCOPUS:33646785076
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II225-II228
BT - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -