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In the context of human action recognition in video sequences, a temporal belief filter based on the Transferable Belief Model is proposed. It ensures a consistency in the temporal belief evolution. The filter is useful to cope with varying video quality and experiment conditions by smoothing belief on actions and solving conflict due to contradictory(More)
The Transferable Belief Model (TBM) relies on belief functions and enables one to represent and combine a variety of knowledge from certain up to ignorance as well as conflict inherent to imperfect data. A lot of applications have used this flexible framework however, in the context of temporal data analysis of belief functions, a few work have been(More)
The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2010 semantic indexing and instance search tasks. For the semantic indexing task, we evaluated a number of different descriptors and tried different fusion strategies, in particular hierarchical fusion. The best(More)
In the context of human action sequence recognition in video sequences, a scheduler of actions is proposed. The Belief Scheduler is based on a Temporal Belief Filter ensuring a consistency in the temporal belief evolution as well as temporal constraints. The Belief Scheduler is inspired from System Science and is used to recognize actions in human activity(More)
This article presents a new method of camera motion classification based on Transferable Belief Model (TBM). It consists in locating in a video the motions of translation and zoom, and the absence of camera motion (i.e static camera). The classification process is based on a rule-based system that is divided into three stages. From a parametric motion(More)
A method for the classification of facial expressions from the analysis of facial deformations is presented. Neutral. The proposed classifier relies on data coming from a contour segmentation technique, which extracts an expression skeleton of facial features (mouth, eyes and eyebrows) and derives simple distance coefficients from every face image of a(More)
An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transfer-able Belief Model (TBM) framework for action recognition. The TBM-based modelling and fusion(More)
This paper presents a system for classifying facial expressions based on a data fusion process relying on the Belief Theory (BeT). Four expressions are considered: joy, surprise, disgust as well as neutral. The proposed system is able to take into account intrinsic doubt about emotion in the recognition process and to handle the fact that each person has(More)