Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face as mouse through visual face tracking
Computer Vision and Image Understanding
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Monocular head pose estimation using generalized adaptive view-based appearance model
Image and Vision Computing
Adaptive view-based appearance models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional eigentemplate learning for sparse template tracker
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
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A novel memory-based particle filter is proposed to achieve robust visual tracking of a target's pose even with large variations in target's position and rotation, i.e. large appearance changes. The memorybased particle filter (M-PF) is a recent extension of the particle filter, and incorporates a memory-based mechanism to predict prior distribution using past memory of target state sequence; it offers robust target tracking against complex motion. This paper extends the M-PF to a unified probabilistic framework for joint estimation of the target's pose and appearance based on memory-based joint prior prediction using stored past pose and appearance sequences. We call it the Memory-based Particle Filter with Appearance Prediction (M-PFAP). A memory-based approach enables generating the joint prior distribution of pose and appearance without explicit modeling of the complex relationship between them. M-PFAP can robustly handle the large changes in appearance caused by large pose variation, in addition to abrupt changes in moving direction; it allows robust tracking under self and mutual occlusion. Experiments confirm that M-PFAP successfully tracks human faces from frontal view to profile view; it greatly eases the limitations of M-PF.