Fusion de données images segmentées à l'aide du formalisme de dempster shafer

Ce papier présente une méthode de classification d'objets non supervisée à l'aide du formalisme de Dempster Shafer, classification basée sur la fusion de données numériques de type différent ou provenant de sources différentes. Pour un attribut donné de l'objet, on ne dispose d'aucune information sur la caractérisation des classes ainsi que du nombre de classes a priori, mais l'on dispose d'un grand nombre de mesures non classées. Une première classification est faite pour chaque attribut, et à chaque classe on associe la confiance (masse d'évidence) pour que l'objet considéré appartienne effectivement à cette classe. En réalisant une extension des distributions de masses aux espaces de discernement associés aux différents attributs, on peut faire une fusion des informations pour obtenir une classification finale tenant compte de tous les attributs

Situation assessment: an end-to-end process for the detection of objects of interest

International audienceIn this article, semi-automatic approaches are developed for wide area situation assessment in near-real-time. The two-step method consists of two granularity levels. The first entity assessment uses a new multi-target tracking algorithm (hybridization of GM-CPHD filter and MHT with road constraints) on GMTI data. The situation is then assessed by detecting objects of interest such as convoys with other data types (SAR, video). These detections are based on Bayesian networks and their credibilistic counterpart

Joint Prediction of Continuous and Discrete States in Time-Series Based on Belief Functions.

International audienceForecasting the future states of a complex system is a complicated challenge that is encountered in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous or discrete: Continuous states generally represent the value of a signal while discrete states generally depict functioning modes reflecting the current degradation. For each case, specific techniques exist. In this paper, we propose an approach based on case-based reasoning that jointly estimates the future values of the continuous signal and the future discrete modes. The main characteristics of the proposed approach are the following: 1) It relies on the K-nearest neighbours algorithm based on belief functions theory; 2) Belief functions allow the user to represent his partial knowledge concerning the possible states in the training dataset, in particular concerning transitions between functioning modes which are imprecisely known; 3) Two distinct strategies are proposed for states prediction and the fusion of both strategies is also considered. Two real datasets were used in order to assess the performance in estimating future break-down of a real system

Joint prediction of observations and states in time-series based on belief functions

International audienceForecasting the future states of a complex system is a complicated challenge that is encountered in many industrial applications covered in the community of Prognostics and Health Management (PHM). Practically, states can be either continuous or discrete: Continuous states generally represent the value of a signal while discrete states generally depict functioning modes reflecting the current degradation. For each case, specific techniques exist. In this paper, we propose an approach based on case-based reasoning that jointly estimates the future values of the continuous signal and the future discrete modes. The main characteristics of the proposed approach are the following: 1) It relies on the K-nearest neighbours algorithm based on belief functions theory; 2) Belief functions allow the user to represent his partial knowledge concerning the possible states in the training dataset, in particular concerning transitions between functioning modes which are imprecisely known; 3) Two distinct strategies are proposed for states prediction and the fusion of both strategies is also considered. Two real datasets were used in order to assess the performance in estimating future break-down of a real system

Spatio-temporal block model for video indexation assistance

International audienceIn the video indexing framework, we have developed a user assistance system in order to define concept models (i.e semantic index) according to features automatically extracted from the video. Because the manual indexing is a long and tedious task, we propose to focus the user attention on pre-selected prototypes that a priori correspond to the searched concepts. The proposed system is decomposed in three steps. In the first one, some basic spatio-temporal blocks are extracted from the video, a particular block being associated to a particular property of one feature. In the second step, a Question/Answer system allows the user to define links between basic blocks in order to define concept block models. And finally, some concept blocks are extracted and proposed as prototypes of the concepts. In this paper, we present the two first steps, particularly the block structure, illustrated by an example of video indexing that corresponds to the concept running in athletic videos

Points d'intérêt spatio-temporels pour la détection de mouvements dans les vidéos

National audienceParmi toutes les caractéristiques qui peuvent être extraites de vidéos, les points d'intérêt spatiotemporels (STIP) sont particulièrement intéressants car ce sont des caractéristiques de bas niveau simples et robustes qui permettent une bonne caractérisation des objets en mouvement. Dans cet article, nous définissons les STIP et analysons leurs propriétés. Puis, les STIP sont utilisés pour détecter des objets en mouvement et pour caractériser les changements spécifiques dans les mouvements de ces objets. Les performances sont étudiées sur des types très différents de vidéos : des séquences d'athlétisme et des séquences de films d'animation

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided

Track Initiation of Low-Earth-Orbit Objects using Statistical Modeling of Sparse Observations

International audienceIn this paper, we investigate a new track initiation technique enabling the use of a low-cost radar system for Low-Earth-Orbit surveillance. This technique is based on a first association of observations with little ambiguity followed by a fast Initial Orbit Determination. This study supports the feasibility of the system as this technique shows a coverage of 84,4% within 6 days, with a combinatorial complexity kept under control when assessed in a realistic multitarget tracking context

Audiovisual data fusion for successive speakers tracking

International audienceIn this paper, a human speaker tracking method on audio and video data is presented. It is applied to con- versation tracking with a robot. Audiovisual data fusion is performed in a two-steps process. Detection is performed independently on each modality: face detection based on skin color on video data and sound source localization based on the time delay of arrival on audio data. The results of those detection processes are then fused thanks to an adaptation of bayesian filter to detect the speaker. The robot is able to detect the face of the talking person and to detect a new speaker in a conversation