Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Nonlinear dynamic shape and appearance models for facial motion tracking
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Co-trained generative and discriminative trackers with cascade particle filter
Computer Vision and Image Understanding
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Our objective is to model the visual manifold of object appearance corresponding to geometric transformation. We learn a generative model for object appearance where the appearance of the object at each new frame is a function that maps from a conceptual representation of the geometric transformation space into the visual manifold. By learning such generative model we can infer the geometric transformation (track) directly from the tracked object appearance. As a result tracking can be achieved in a closed form and therefore can be done very efficiently.