we consider situations in which the object is unknown the only way of doing pose estimation is then building a map between image measurements (features) and configurations (poses) directly from the data this can be done in a training stage, where an approximate parameter space is collected as ground truth configuration values produced by an oracle (motion capture) hidden Markov models (HMMs) provide a way to the learn feature-pose maps automatically through the EM algorithm for each state of the HMM a Gaussian likelihood is set up on the feature range this partitions the feature range into n regions (approximate feature space) and is associated with a refining from Y to Q PERFORMANCES PERFORMANCES pose estimate for the leg exp EXPERIMENTS ON HUMAN BODY TRACKING EXPERIMENTS ON HUMAN BODY TRACKING FEATURE-POSE MAPS AND HMMs FEATURE-POSE MAPS AND HMMs FEATURE EXTRACTION two experiments: four markers on right arm, eight markers on legs we built evidential models for the two separate views and compared them with an overall model EVIDENTIAL MODEL EVIDENTIAL MODEL EVIDENTIAL MODELING FOR POSE ESTIMATION Fabio Cuzzolin, Computer Science Department, UCLA; Ruggero Frezza, DEI, Universita di Padova EVIDENTIAL MODELING FOR POSE ESTIMATION Fabio Cuzzolin, Computer Science Department, UCLA; Ruggero Frezza, DEI, Universita di Padova 4 nd INTERNATIONAL SYMPOSIUM ON IMPRECISE PROBABILITIES AND THEIR APPLICATIONS, ISIPTA05 Carnegie Mellon University, Pittsburgh, USA, July TRAINING A BOTTOM-UP MODEL TRAINING A BOTTOM-UP MODEL the collection of approximate parameter space and feature spaces, linked by the refining maps learned in the training stage form an evidential model of the object the evidential model can then be used thereafter to estimate the pose of the body when new images become available BOTTOM LINES we want to estimate the configuration (pose) of an we want to estimate the configuration (pose) of an unknown object from a sequence of images unknown object from a sequence of images model-free pose estimation requires to build maps from model-free pose estimation requires to build maps from image measurements (features) to poses image measurements (features) to poses different features have to be integrated to increase different features have to be integrated to increase accuracy and robustness of the estimation accuracy and robustness of the estimation the evidential model provides such a framework the evidential model provides such a framework MODEL-FREE POSE ESTIMATION MODEL-FREE POSE ESTIMATION pose estimation: reconstruction of the actual pose of a moving object by processing the sequence of images taken during its motion model-based pose estimation: a kinematic model of the body is known and used to help the estimation the estimate of component 9 of the pose vector shows a neat improvement when using the comprehensive model the silhouette of the body of interest is detected by colorimetric analysis the bounding box containing the object is found feature vector = collection of the coordinates of the vertices of the box example: Rehg and Kanade pose = angles between links of the fingers TRAINING : the body moves in front of the camera(s), while a sequence of poses is provided by a motion capture system. Then some features are computed from the images these feature sequences are passed to an HMM with n states yielding: the approximate feature spaces the maps between features and poses two views acquired through DV cameras 1 ESTIMATION : the body performs new movements in front of the camera(s), and for each available image the features are computed as before the likelihoods of the features are transformed into belief functions those measurement functions are projected onto Q and combined a pointwise estimate of the object pose is computed by pignistic or relative plausibility transformation 2 comparison between visual estimate and real view