TY - GEN
T1 - Implementation of a modular real-time feature-based architecture applied to visual face tracking
AU - Castañeda, Benjamín
AU - Luzanov, Yuriy
AU - Cockburn, Juan C.
PY - 2004
Y1 - 2004
N2 - This paper presents a modular real-time feature-based visual tracking architecture where each feature of an object is tracked by one module. A data fusion stage collects the information from various modules exploiting the relationship among features to achieve robust detection and visual tracking. This architecture takes advantage of the temporal and spatial information available in a video stream. Its effectiveness is demonstrated in a face tracking system that uses eyes and lips as features. In the architecture implementation, each module has a pre-processing stage that reduces the number of image regions that are candidates for eyes and lips. Support Vector Machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking. The geometric relation between features is used in the data fusion stage to combine the information from different modules to improve tracking.
AB - This paper presents a modular real-time feature-based visual tracking architecture where each feature of an object is tracked by one module. A data fusion stage collects the information from various modules exploiting the relationship among features to achieve robust detection and visual tracking. This architecture takes advantage of the temporal and spatial information available in a video stream. Its effectiveness is demonstrated in a face tracking system that uses eyes and lips as features. In the architecture implementation, each module has a pre-processing stage that reduces the number of image regions that are candidates for eyes and lips. Support Vector Machines are then used in the classification process, whereas a combination of Kalman filters and template matching is used for tracking. The geometric relation between features is used in the data fusion stage to combine the information from different modules to improve tracking.
UR - https://www.scopus.com/pages/publications/10044237576
U2 - 10.1109/ICPR.2004.1333730
DO - 10.1109/ICPR.2004.1333730
M3 - Conference contribution
AN - SCOPUS:10044237576
SN - 0769521282
T3 - Proceedings - International Conference on Pattern Recognition
SP - 167
EP - 170
BT - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Pattern Recognition, ICPR 2004
Y2 - 23 August 2004 through 26 August 2004
ER -