TY - GEN
T1 - Model-based human motion analysis in monocular video
AU - Lok, W. W.
AU - Chan, K. L.
PY - 2005
Y1 - 2005
N2 - Tracking human motion in monocular video is a challenging problem in computer vision. It has found a wide range of applications such as visual surveillance, virtual reality, sports science, etc. This project aims to develop a model-based human motion analysis system that can track human movement in monocular image sequence with minimum constraint. No markers or sensors are attached to the subject. Given a clip of the video, the first step is to manually fit the 3D human model to the subject in the first frame of the video. Then background subtraction is used to extract the human silhouette. We propose the silhouette chamfer as the main matching feature. Chamfer distance measure is carried out on the extracted subject silhouette. The silhouette chamfer contains both the chamfer distance and region information. Finally, we use discrete Kalman filter to predict the pose of the subject in each image frame. The update step uses Broydent's method to optimize the predicted pose to fit the person's silhouette by using the cost function. We use the gait database SOTON to test our system. The image sequences contain human walking in both the indoor and outdoor environment. The motion tracking results demonstrate that our system has an encouraging performance. © 2005 IEEE.
AB - Tracking human motion in monocular video is a challenging problem in computer vision. It has found a wide range of applications such as visual surveillance, virtual reality, sports science, etc. This project aims to develop a model-based human motion analysis system that can track human movement in monocular image sequence with minimum constraint. No markers or sensors are attached to the subject. Given a clip of the video, the first step is to manually fit the 3D human model to the subject in the first frame of the video. Then background subtraction is used to extract the human silhouette. We propose the silhouette chamfer as the main matching feature. Chamfer distance measure is carried out on the extracted subject silhouette. The silhouette chamfer contains both the chamfer distance and region information. Finally, we use discrete Kalman filter to predict the pose of the subject in each image frame. The update step uses Broydent's method to optimize the predicted pose to fit the person's silhouette by using the cost function. We use the gait database SOTON to test our system. The image sequences contain human walking in both the indoor and outdoor environment. The motion tracking results demonstrate that our system has an encouraging performance. © 2005 IEEE.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33646809766&origin=recordpage
U2 - 10.1109/ICASSP.2005.1415500
DO - 10.1109/ICASSP.2005.1415500
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780388747
SN - 9780780388741
VL - II
SP - II697-II700
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -