TY - GEN
T1 - Genetic CONDENSATION for motion tracking
AU - Ye, Zhu
AU - Liu, Zhi-Qiang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - Tracking is a particularly important issue in human motion analysis since it serves as a means to prepare data for pose estimation and action recognition. The CONDENSATION algorithm is a kind of conditional density propagation method for motion tracking. This algorithm combines factored sampling with learned dynamic models to propagate an entire probability distributes for object position and shape over time. It can accomplish highly robust tracking of object motion. However, it usually requires a large number of samples to ensure a fair maximum likelihood estimation of the current state. The important problem of the CONDENSATION algorithm is to choose proper samples to approach the actual samples position. In this paper, we use the mutation and crossover operators of the genetic algorithm to find more appropriate samples by calculating weights. Accordingly, we can solve the heavy demand of samples in the CONDENSATION algorithm. Eventually, we can improve robustness, accuracy and flexibility in CONDENSATION for visual tracking. © 2005 IEEE.
AB - Tracking is a particularly important issue in human motion analysis since it serves as a means to prepare data for pose estimation and action recognition. The CONDENSATION algorithm is a kind of conditional density propagation method for motion tracking. This algorithm combines factored sampling with learned dynamic models to propagate an entire probability distributes for object position and shape over time. It can accomplish highly robust tracking of object motion. However, it usually requires a large number of samples to ensure a fair maximum likelihood estimation of the current state. The important problem of the CONDENSATION algorithm is to choose proper samples to approach the actual samples position. In this paper, we use the mutation and crossover operators of the genetic algorithm to find more appropriate samples by calculating weights. Accordingly, we can solve the heavy demand of samples in the CONDENSATION algorithm. Eventually, we can improve robustness, accuracy and flexibility in CONDENSATION for visual tracking. © 2005 IEEE.
KW - CONDENSATION algorithm
KW - Factored sampling
KW - Genetic algorithm
KW - Metroplis algorithm
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=28444458849&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-28444458849&origin=recordpage
U2 - 10.1109/ICMLC.2005.1527924
DO - 10.1109/ICMLC.2005.1527924
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780390911
SN - 078039092
SN - 9780780390928
VL - 9
T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
SP - 5542
EP - 5547
BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
PB - IEEE Computer Society
T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005
Y2 - 18 August 2005 through 21 August 2005
ER -