IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Tracking and Learning Graphs and Pose on Image Sequences of Faces
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Real-Time Tracking of Multiple Persons
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Active Appearance Models Revisited
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking multiple people with recovery from partial and total occlusion
Pattern Recognition
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces; (b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return; (c) run with a high computation efficiency, run at about 20 frames/s.