Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable BeliefModel

We present an automatic human shape-motion analysis method based on a fusion architecture for human action and activity recognition in athletic videos. Robust shape and motion features are extracted from human detection and tracking. The features are combined within the Transferable Belief Model (TBM framework for two levels of recognition. The TBM-based modelling of the fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions are roughly recognized. Then, in a fine step, an action sequence recognition method is used to discriminate activities. Belief on actions are made smooth by a Temporal Credal Filter and action sequences, i.e. activities, are recognized using a state machine, called belief scheduler, based on TBM. The belief scheduler is also exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of actions (running, jumping, falling and standing) and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activities and feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposed recognition approach based on TBM

Belief theory applied to facial expressions classification

Abstract. A novel and efficient approach to facial expression classification based on the belief theory and data fusion is presented and discussed. The considered expressions correspond to three (joy, surprise, disgust) of the six universal emotions as well as the neutral expression. A robust contour segmentation technique is used to generate an expression skeleton with facial permanent features (mouth, eyes and eyebrows). This skeleton is used to determine the facial features deformations occurring when an expression is present on the face defining a set of characteristic distances. In order to be able to recognize “pure ” as well as “mixtures ” of facial expressions, a belief-theory based fusion process is proposed. The performances and the limits of the proposed recognition method are highlighted thanks to the analysis of a great number of results on three different test databases: the Hammal-Caplier database, the Cohn-Kanade database and the Cottrel database. Preliminary results demonstrate the interest of the proposed approach, as well as its ability to recognize non separable facial expressions.