Identification de modèles physiques et de contrôleurs en animation

Particle-based mass-bond models are well adapted to motion modeling and image animation. A practical limitation comes from the use of purely visco-elastic bonds which only allow passive, energetically dissipative behaviour. Another difficulty is how to built and adjust such a model in order to ensure realistic synthetic motion. The main goal of this thesis is to extend the domain of applicability of particle-bond models through the introduction of motricity. To this end two variants of active bonds (muscles) have been introduced. Their theoretical propertieq, connections with ACROE’s FGT (force-gesture transducer) and proprioception are discussed.Les modèles à base de particules et de liaisons sont bien adaptés aux simulations du mouvement sur ordinateur, notamment en synthèse d’images animées. Une limitation pratique provient de l’utilisation de liaisons purement visco-élastiques qui restreint la classe des mouvements autorisés aux comportements passifs et énergétiquement dissipatifs. Un autre point dur de la mise en œuvre de ces modèles est la méthode de construction et d’identification garantissant le réalisme des comportements synthétisés. L’objet principal de cette thèse est d’élargir le domaine d’applicabilité des modèles masses-liaisons par l’introduction de la motricité. Pour cela, nous avons introduit deux variantes de liaisons actives (muscles) dont nous étudions les caractéristiques, leurs liens avec le TGR (Transducteur Gestuel Rétroactif) de l’ACROE et la proprioception musculaire

Introducing New AdaBoost Features for Real-Time Vehicle Detection

International audienceThis paper shows how to improve the real-time object detection in complex robotics applications, by exploring new visual features as AdaBoost weak classifiers. These new features are symmetric Haar filters (enforcing global horizontal and vertical symmetry) and N-connexity control points. Experimental evaluation on a car database show that the latter appear to provide the best results for the vehicle-detection problem

General Road Detection Algorithm, a Computational Improvement

International audienceThis article proposes a method improving Kong et al. algorithm called Locally Adaptive Soft-Voting (LASV) algorithm described in " General road detection from a single image ". This algorithm aims to detect and segment road in structured and unstructured environments. Evaluation of our method over different images datasets shows that it is speeded up by up to 32 times and precision is improved by up to 28% compared to the original method. This enables our method to come closer the real time requirements

Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features

International audienceThis paper deals with real-time visual detection, by mono-camera, of objects categories such as cars and pedestrians. We report on improvements that can be obtained for this task, in complex applications such as advanced driving assistance systems, by using new visual features as adaBoost weak classifiers. These new features, the “connected controlpoints” have recently been shown to give very good results on real-time visual rear car detection. We here report on results obtained by applying these new features to a public lateral car images dataset, and a public pedestrian images database. We show that our new features consistently outperform previously published results on these databases, while still operating fast enough for real-time pedestrians and vehicles detection

Unsupervised clustering of hyperspectral images of brain tissues by hierarchical non-negative matrix factorization

International audienceHyperspectral images of high spatial and spectral resolutions are employed to perform the challenging task of brain tissue characterization and subsequent segmentation for visualization of in-vivo images. Each pixel is a high-dimensional spectrum. Working on the hypothesis of pure-pixels on account of high spectral resolution, we perform unsupervised clustering by hierarchical non-negative matrix factorization to identify the pure-pixel spectral signatures of blood, brain tissues, tumor and other materials. This subspace clustering was further used to train a random forest for subsequent classification of test set images constituent of in-vivo and ex-vivo images. Unsupervised hierarchical clustering helps visualize tissue structure in in-vivo test images and provides a inter-operative tool for surgeons. Furthermore the study also provides a preliminary study of the classification and sources of errors in the classification process

Vidéosurveillance intelligente : ré-identification de personnes par signature utilisant des descripteurs de points d'intérêt collectés sur des séquences

National audienceNous présentons et évaluons une méthode de ré-identification de personnes pour les systèmes de surveillance multicaméras. Notre approche utilise la mise en correspondance de signatures fondées sur les descripteurs de points d'intérêt collectés sur de courtes séquences vidéos. Une des originalités de notre travail est d'accumuler les points d'intérêt à des instants suffisamment espacés durant le suivi de personne, de façon à capturer dans la signature la variabilité d'apparence des personnes. Une première évaluation expérimentale a été effectuée sur une base publique d'enregistrements à basse résolution dans un centre commercial, et les performances de re-identification sont très prometteuses (une précision de 82% pour un rappel de 78%). De plus, notre technique de ré-identification est particulièrement rapide : ~1/8 s pour une requête à comparer à 10 personnes vues précédemment, et surtout une dépendance logarithmique avec le nombre de modèles stockés, de sorte que la ré-identification parmi des milliers de personnes prendrait moins de ¼ s de calcul

Interest points harvesting in video sequences for efficient person identification

International audienceWe propose and evaluate a new approach for identification of persons, based on harvesting of interest point descriptors in video sequences. By accumulating interest points on several sufficiently time-spaced images during person silhouette or face tracking within each camera, the collected interest points capture appearance variability. Our method can in particular be applied to global person re-identification in a network of cameras. We present a first experimental evaluation conducted on a publicly available set of videos in a commercial mall, with very promising inter-camera pedestrian reidentification performances (a precision of 82% for a recall of 78%). Our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making re-identification among hundreds of persons computationally feasible in less than ~ 1/5s second. Finally, we also present a first feasibility test for on-the-fly face recognition, with encouraging results

Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences

International audienceWe present and evaluate a person re-identification scheme for multi-camera surveillance system. Our approach uses matching of signatures based on interest-points descriptors collected on short video sequences. One of the originalities of our method is to accumulate interest points on several sufficiently time-spaced images during person tracking within each camera, in order to capture appearance variability. A first experimental evaluation conducted on a publicly available set of low-resolution videos in a commercial mall shows very promising inter-camera person re-identification performances (a precision of 82% for a recall of 78%). It should also be noted that our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making reidentification among hundreds of persons computationally feasible in less than ~ 1/5 second

CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation

Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world. In this paper, we use them as an implicit map of a given scene and propose a camera relocalization algorithm tailored for this representation. The proposed method enables to compute in real-time the precise position of a device using a single RGB camera, during its navigation. In contrast with previous work, we do not rely on pose regression or photometric alignment but rather use dense local features obtained through volumetric rendering which are specialized on the scene with a self-supervised objective. As a result, our algorithm is more accurate than competitors, able to operate in dynamic outdoor environments with changing lightning conditions and can be readily integrated in any volumetric neural renderer.Comment: Accepted to ICCV 202

ImPosing: Implicit Pose Encoding for Efficient Visual Localization

We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or a 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Very large environments force competitors to store gigabytes of map data, whereas our method is very compact independently of the reference database size. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.Comment: Accepted at WACV 202