Compensation de mouvement sur maillage rectangulaire

Ce papier décrit une méthode de partitionnement d'images en rectangles, ainsi que son utilisation pour la prédiction d'images par compensation de mouvement. Le partitionnement rectangulaire présente l'avantage d'être correctement adapté à la texture de l'image et de générer des blocs de tailles différentes. En prenant en compte ces propriétés, nous testons différentes adaptations des algorithmes de Block-Matching dans le but d'améliorer la qualité de reconstruction des images. Trois méthodes sont décrites, ainsi qu'une optimisation de l'étape d'estimation de mouvement. Enfin, nous testons la robustesse de ces algorithmes pour différentes tailles de périodes de rafraîchissement

How consumption prescriptions affect food practices: Assessing the roles of household resources and life-course events

International audienceFood consumption has become the subject of many prescriptions that aim to improve consumers’ health and protect the environment. This study examined recent changes in food practices that occurred in response to prescriptions. Based on practice theories, we assume that links that connect practices with prescriptions result from evolving social interactions. Consistent with the life-course perspective, we focus on distinctions between public prescriptions and standards that individuals consider relevant to their lives. We rely on quantitative data and the results of qualitative fieldwork conducted in France. Our results suggest that consumers may change food practices when they reach turning points in their lives. They may reconsider resources, skills and standards. Middle- and upper-class individuals are more likely to adopt standards consistent with public prescriptions. Possible explanations are that they trust expert knowledge sources, their social networks are less stable and smaller gaps exist between their standards and prescriptions

Association spatio-temporelle avec données manquantes par minimisation d'énergie

Le suivi multi-cible en ligne nécessite la résolution de deux problèmes : l'association de données et l'évaluation en ligne des paramètres du modèle dynamique. Dans cet article, on propose une nouvelle méthode d'association de données, utilisant une énergie à minimiser, définie par des critères géométriques permettant de suivre plusieurs objets, déformables ou non, avec mouvements non linéaires et non constants. L'énergie qu'on propose ne nécessite aucun paramètre ni connaissance a priori et ses composantes sont extraites à partir de représentations géométriques (surface et distance) construites avec des mesures et des prédictions. On montre que cette méthode est robuste pour associer correctement une ou plusieurs mesures lorsqu'elles sont équidistantes d'une cible et qu'elle gère les problèmes de données manquantes

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

Predictive uncertainty estimation is essential for safe deployment of Deep Neural Networks in real-world autonomous systems. However, disentangling the different types and sources of uncertainty is non trivial for most datasets, especially since there is no ground truth for uncertainty. In addition, while adverse weather conditions of varying intensities can disrupt neural network predictions, they are usually under-represented in both training and test sets in public datasets.We attempt to mitigate these setbacks and introduce the MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. MUAD allows to better assess the impact of different sources of uncertainty on model performance. We conduct a thorough experimental study of this impact on several baseline Deep Neural Networks across multiple tasks, and release our dataset to allow researchers to benchmark their algorithm methodically in adverse conditions. More visualizations and the download link for MUAD are available at https://muad-dataset.github.io/.Comment: Accepted at BMVC 202

Tree-structured image difference for fast histogram and distance between histograms computation

International audienceIn this paper we present a new method for fast histogram computing and its extension to bin to bin histogram distance computing. The idea consists in using the information of spatial differences between images, or between regions of images (a current one and a reference one), and encoding it into a specific data structure: a tree. The histogram of the current image or of one of its regions is then computed by updating the histogram of the reference one using the temporal data stocked into the tree. With this approach, we never need to store any of the current histograms, except the reference image ones, as a preprocessing step. We compare our approach with the well-known Integral Histogram one, and obtain better results in terms of processing time while reducing the memory footprint. We show theoretically and with experimental results the superiority of our approach in many cases. We also extend our idea to the computation of the Bhattacharyya distance between two histograms, using a similar incremental approach that also avoid current histogram computations: we just need histograms of the reference image, and spatial differences between the reference and the current image to compute this distance using an updating process. Finally, we demonstrate the advantages of our approach on a real visual tracking application using a particle filter framework by improving its correction step computation time

Tree-structured temporal information for fast histogram computation

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Estimation de densités non paramétriques et multimodales par permutation de sous-particules. Application au suivi d'un ou de plusieurs objets synthétiques articulés

National audienceDans cet article, nous proposons une approche originale d’estimation séquentielle de densités non paramétriques définies dans des espaces de grande dimension, dans le cadre méthodologique du filtrage particulaire. En exploitant les indépendances conditionnelles de l’espace d’état, nous proposons de permuter des sous-ensembles indépendants de particules de manière à générer un nouvel ensemble échantillonnant mieux cet espace. Nous intégrons cette approche dans deux versions classiques du filtre particulaire : celui avec échantillonnage partitionné et celui à recuit simulé de manière à prouver son efficacité. Nous comparons notre modèle aux approches classiques dans le cadre de l’estimation des densités d’objets synthétiques articulés. Nous montrons que notre approche diminue à la fois les erreurs d’estimation et les temps de traitement

A survey of datasets for visual tracking

International audienceFor fifteen years now, visual tracking has been a very active research area of the computer vision community. But an increasing amount of works can be observed in the last five years. This has led to the development of numerous algorithms that can deal with more and more complex video sequences. Each of them has its own strengths and weaknesses. That is the reason why it becomes necessary to compare those algorithms. For this purpose, some datasets dedicated to visual tracking as well as, sometimes, their ground truth annotation files, are regularly made publicly available by researchers. However, each dataset has its own speci-ficities and is sometimes dedicated to test the ability of some algorithms to tackle only one or a few specific visual tracking subproblems. This article provides an overview of some of the datasets that are most used by the visual tracking community, but also of others that address specific tasks. We also propose a cartography of these datasets from a novel perspective, namely that of the difficulties the datasets present for visual tracking