On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Adaptive visual tracking and recognition using particle filters
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Object Tracking with Target and Background Samples
IEICE - Transactions on Information and Systems
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Face detection and tracking in a video by propagating detection probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a method for solving face tracking problem under varying poses. The proposed method is based on Particle Filter (PF) using a combination of adaptive template and background detection. Difficulties of face tracking problem are arbitrary movement and continuously-changing shape of facial objects. It is necessary to select a suitable template online for a robust tracking. In our proposed method, the background detection utilizes the online histogram at each position in each frame to classify pixels of moving object and background. Our method is independent of background change. It is usually slower than that of object movements. This background detection helps to confine tracking regions in each frame for accuracy improvement. The adaptive template method uses an adequate template updated time by time for changes of facial pose. As a result, facial template can be adaptive to a rather large difference of facial pose. Our proposed method can run online with 5 frames/s and return a robust result of face positions in video sequences where facial object changes from frontal to left, right, up, and down poses fast and continuously.