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Published byAshton Sinclair Modified over 2 years ago

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Evidential modeling for pose estimation Fabio Cuzzolin, Ruggero Frezza Computer Science Department UCLA

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Myself gesture recognition Masters thesis on gesture recognition at the University of Padova theory of evidence Ph.D. thesis on the theory of evidence Post-doc in Milan with the Image and Sound Processing group Post-doc at UCLA in the Vision Lab

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My past work… geometric approach geometric approach to the theory of belief functions space of belief functions geometry of Dempsters rule

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.. again.. algebra of compatible frames linear independence on lattices action recognition and object tracking metrics on the space of dynamical models

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… and todays talk the pose estimation problem model-free pose estimation evidential model experimental results

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Pose estimation pose estimating the pose (internal configuration) of a moving body from the available images features salient image measurements: features t=0t=T CAMERA t=0t=T

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Model-based estimation a-priori model if you have an a-priori model of the object.... you can exploit it to help (or drive) the estimation example: kinematic model

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Model-free estimation if you do not have any information about the body.. the only way to do inference is to learn a map learn a map between features and poses directly from the data training stage this can be done in a training stage

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Collecting training data motion capture system 3D locations of markers = pose

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Training data when the object performs some significant movements in front of the camera … … a finite collection of configuration values are provided by the motion capture system … while a sequence of features is computed from the image(s) q q yy 1 1 T T

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Learning feature-pose maps Hidden Markov models Hidden Markov models provide a way to build feature-pose maps from the training data approximate feature space a Gaussian density for each state is set up on the feature space -> approximate feature space map map between each region and the set of training poses q k with feature value y k inside it

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Evidential model approximate feature spaces.... and approximate parameter space.. family of compatible frames: the evidential model.. form a family of compatible frames: the evidential model

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Estimation these belief functions are projected onto the approximate parameter space.... and combined through Dempsters rule a point-wise estimate of the pose is obtained by probabilistic approximation new features are represented as belief functions..

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Human body tracking two experiments, two views four markers on the right arm six markers on both legs

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Feature extraction three steps: original image, color segmentation, bounding box

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Performances comparison of three models: left view only, right view only, both views pose estimation yielded by the overall model estimate associated with the right model left model ground truth

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Estimation errors Euclidean distance between real and predicted marker position marker 4 3cm marker 2 8cm

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Visual estimate compares the actual image with the weighted sum of the training images

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Conclusions pose estimation of unknown objects is a difficult task a bottom-up model has to be built from the data in a training session the DS framework allows to formalize the idea of feature-pose maps in a natural way through the notion of compatible frames Dempsters combination provides a method to integrate features to increase robustness

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