Uncertainty Control for Reliable Video Understanding on Complex Environments

International audienceThe most popular applications for video understanding are those related to video-surveillance (e.g. alarms, abnormal behaviours, expected events, access control). Video understanding has several other applications of high impact to the society as medical supervision, traffic control, violent acts detection, crowd behaviour analysis, among many others. We propose a new generic video understanding approach able to extract and learn valuable information from noisy video scenes for real-time applications. This approach comprises motion segmentation, object classification, tracking and event learning phases. This work is focused on building the first fundamental blocks allowing a proper management of uncertainty of data in every phase of the video understanding process. The main contributions of the proposed approach are: (i) a new algorithm for tracking multiple objects in noisy environments, (ii) the utilisation of reliability measures for modelling uncertainty in data and for proper selection of valuable information extracted from noisy data, (iii) the improved capability of tracking to manage multiple visual evidence-target associations, (iv) the combination of 2D image data with 3D information in a dynamics model governed by reliability measures for proper control of uncertainty in data, and (v) a new approach for event recognition through incremental event learning, driven by reliability measures for selecting the most stable and relevant data

Preliminary observations on Schistosoma curassoni Brumpt, 1931 in Niger

Il est rapporté les résultats de deux enquêtes sur les schistosomiases animales (#S. bovis et #S. curassoni) du bétail, réalisées dans des abattoirs du Nige

Tracking HOG Descriptors for Gesture Recognition

International audienceWe introduce a new HoG (Histogram of Oriented Gradients) tracker for Gesture Recognition. Our main contribution is to build HoG trajectory descriptors (representing local motion) which are used for gesture recognition. First, we select for each individual in the scene a set of corner points to determine textured regions where to compute 2D HoG descriptors. Second, we track these 2D HoG descriptors in order to build temporal HoG descriptors. Lost descriptors are replaced by newly detected ones. Finally, we extract the local motion descriptors to learn offline a set of given gestures. Then, a new video can be classified according to the gesture occurring in the video. Results shows that the tracker performs well compared to KLT tracker [1]. The generated local motion descriptors are validated through gesture learning-classification using the KTH action database [2]

Simple modelling and control of plasma current profile

International audienceThe purpose of this paper is to present a simplified model and control law of the current and temperature profile in a tokamak plasma. Based on a description of the plasma as a magnetised uid, the model is expressed in the form of coupled one dimensional transport-diffusion equations. A simple feedback is used to obtain a given stationary profile. The numerical simulations are done in the Scilab/Scicos environment

La schistosomose urinaire dans le massif saharien de l'Aïr (République du Niger)

Dans deux villages de l'Aïr (République du Niger), les auteurs ont trouvé une prévalence globale de #Schistosoma haematobium$de 24,1$ est l'hôte intermédiaire des schistosomes. Le rôle de ce mollusque dans la transmission de la schistosomose urinaire à Timia n'a pas été mis en évidence. Cemio de #Bulinus senegalensis$ présent dans les deux localités reste encore à préciser. (Résumé d'auteur

Gender estimation based on smile-dynamics

International audienceAutomated gender estimation has numerous applications including video surveillance, human computer-interaction, anonymous customized advertisement and image retrieval. Most commonly, the underlying algorithms analyze the facial appearance for clues of gender. In this work we propose a novel method for gender estimation, which exploits dynamic features gleaned from smiles and we proceed to show that (a) facial dynamics incorporate clues for gender dimorphism, and (b) that while for adult individuals appearance features are more accurate than dynamic features, for subjects under 18 years old facial dynamics can outperform appearance features. In addition , we fuse proposed dynamics-based approach with state-of-the-art appearance based algorithms, predominantly improving appearance-based gender estimation performance. Results show that smile-dynamics include pertinent and complementary to appearance gender information

Real-time reliability measure-driven multi-hypothesis tracking using 2D and 3D features

International audienceWe propose a new multi-target tracking approach, which is able to reliably track multiple objects even with poor segmentation results due to noisy environments. The approach takes advantage of a new dual object model combining 2D and 3D features through reliability measures. In order to obtain these 3D features, a new classifier associates an object class label to each moving region (e.g. person, vehicle), a parallelepiped model and visual reliability measures of its attributes. These reliability measures allow to properly weight the contribution of noisy, erroneous or false data in order to better maintain the integrity of the object dynamics model. Then, a new multi-target tracking algorithm uses these object descriptions to generate tracking hypotheses about the objects moving in the scene. This tracking approach is able to manage many-to-many visual target correspondences. For achieving this characteristic, the algorithm takes advantage of 3D models for merging dissociated visual evidence (moving regions) potentially corresponding to the same real object, according to previously obtained information. The tracking approach has been validated using video surveillance benchmarks publicly accessible. The obtained performance is real time and the results are competitive compared with other tracking algorithms, with minimal (or null) reconfiguration effort between different videos