TY - JOUR
T1 - Multiple human tracking using particle filter with Gaussian process dynamical model
AU - Wang, Jing
AU - Yin, Yafeng
AU - Man, Hong
PY - 2008
Y1 - 2008
N2 - We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a high-dimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will then be used to classify object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter. In addition, Histogram-Bhattacharyya, GMM Kullback-Leibler, and the rotation invariant appearance models are employed, respectively, and compared in the particle filter as complimentary features to coordinate data used in GPDM. The simulation results demonstrate that the approach can track more than four targets with reasonable runtime overhead and performance. In addition, it can successfully deal with occasional missing frames and temporary occlusion.
AB - We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a high-dimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will then be used to classify object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter. In addition, Histogram-Bhattacharyya, GMM Kullback-Leibler, and the rotation invariant appearance models are employed, respectively, and compared in the particle filter as complimentary features to coordinate data used in GPDM. The simulation results demonstrate that the approach can track more than four targets with reasonable runtime overhead and performance. In addition, it can successfully deal with occasional missing frames and temporary occlusion.
UR - http://www.scopus.com/inward/record.url?scp=58149122717&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58149122717&partnerID=8YFLogxK
U2 - 10.1155/2008/969456
DO - 10.1155/2008/969456
M3 - Article
AN - SCOPUS:58149122717
SN - 1687-5176
VL - 2008
JO - Eurasip Journal on Image and Video Processing
JF - Eurasip Journal on Image and Video Processing
M1 - 969456
ER -