TY - GEN
T1 - Human motion change detection by hierarchical Gaussian process dynamical model with particle filter
AU - Yin, Yafeng
AU - Man, Hong
AU - Wang, Jing
AU - Yang, Guang
PY - 2010
Y1 - 2010
N2 - Human motion change detection is a challenging task for a surveillance sensor system. Major challenges include complex scenes with a large amount of targets and confusors, and complex motion behaviors of different human objects. Human motion change detection and understanding have been intensively studied over the past decades. In this paper, we present a Hierarchical Gaussian Process Dynamical Model (HGPDM) integrated with particle filter tracker for humanmotion change detection. Firstly, the high dimensional human motion trajectory training data is projected to the low dimensional latent space with a two-layer hierarchy. The latent space at the leaf node in bottom layer represents a typical humanmotion trajectory, while the root node in the upper layer controls the interaction and switching among leaf nodes. The trained HGPDM will then be used to classify test object trajectories which are captured by the particle filter tracker. If the motion trajectory is different from the motion in the previous frame, the root node will transfer the motion trajectory to the corresponding leaf node. In addition, HGPDM can be used to predict the next motion state, and provide Gaussian process dynamical samples for the particle filter framework. The experiment results indicate that our framework can accurately track and detect the human motion changes despite of complex motion and occlusion. In addition, the sampling in the hierarchical latent space has greatly improved the efficiency of the particle filter framework.
AB - Human motion change detection is a challenging task for a surveillance sensor system. Major challenges include complex scenes with a large amount of targets and confusors, and complex motion behaviors of different human objects. Human motion change detection and understanding have been intensively studied over the past decades. In this paper, we present a Hierarchical Gaussian Process Dynamical Model (HGPDM) integrated with particle filter tracker for humanmotion change detection. Firstly, the high dimensional human motion trajectory training data is projected to the low dimensional latent space with a two-layer hierarchy. The latent space at the leaf node in bottom layer represents a typical humanmotion trajectory, while the root node in the upper layer controls the interaction and switching among leaf nodes. The trained HGPDM will then be used to classify test object trajectories which are captured by the particle filter tracker. If the motion trajectory is different from the motion in the previous frame, the root node will transfer the motion trajectory to the corresponding leaf node. In addition, HGPDM can be used to predict the next motion state, and provide Gaussian process dynamical samples for the particle filter framework. The experiment results indicate that our framework can accurately track and detect the human motion changes despite of complex motion and occlusion. In addition, the sampling in the hierarchical latent space has greatly improved the efficiency of the particle filter framework.
UR - http://www.scopus.com/inward/record.url?scp=78449292619&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78449292619&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2010.55
DO - 10.1109/AVSS.2010.55
M3 - Conference contribution
AN - SCOPUS:78449292619
SN - 9780769542645
T3 - Proceedings - IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010
SP - 307
EP - 314
BT - Proceedings - IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010
T2 - 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010
Y2 - 29 August 2010 through 1 September 2010
ER -