Adaptive filter theory
Three-dimensional motion computation and object segmentation in a long sequence of stereo frames
International Journal of Computer Vision
Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Design and Use of Linear Models for Image Motion Analysis
International Journal of Computer Vision
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning Parameterized Models of Image Motion
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learned Temporal Models of Image Motion
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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Parametric models are widely used in motion analysis. Traditionally, affine or learned models are adopted. Here, we propose the use of a set of linear models that dynamically adjust their properties to approximate first-order structures in noisy optic flow fields. Each model is generated by the evolution of a recursive network that can be used as a process equation of a multiple model Kalman Filter. The presence of a model is checked by computing the consistence between the observations (data) and the predictions (model). In each image region, for each model, a probability value can be computed, on which to base motion analysis. Experimental results on multiple motion detection problems and facial expressions analysis validate the approach. The algebraic transformations relating our linear descriptors with the traditional affine models are discussed.