Unsupervised dense crowd detection by multiscale texture analysis

International audienceThis study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised

Clinical translation of the assets of biomedical engineering - a retrospective analysis with looks to the future

Introduction: Biomedical-engineering (BME) plays a major role in modern medicine. Many BME-based assets have been brought to clinical translation in the twentieth century, but translation currently stagnates. Here, we compare the impact of past and present scientific, economic and societal climates on the translation of BME-based assets, in order to provide the BME-community with incentives to address current stagnation. Areas covered: In the twentieth century, W.J. Kolff brought kidney dialysis, the total artificial heart, artificial vision and limbs to clinical application. This success raises the question whether Kolff and other past giants of clinical translation had special mind-sets, or whether their problem selection, their training, or governmental and regulatory control played roles. Retrospective analysis divides the impact of BME-based assets to clinical application into three periods: 1900-1970: rapid translation from bench-to-bedside, 1970-1990: new diseases and increased governmental control, and the current translational crisis from 1990 onward. Expert opinion: Academic and societal changes can be discerned that are concurrent with BME's translational success: mono-disciplinary versus multi-disciplinary training, academic reward systems based on individual achievements versus team achievements with strong leadership, increased governmental and regulatory control, and industrial involvement. From this, recommendations can be derived for accelerating clinical translation of BME-assets

A Trans-Atlantic Perspective on Stagnation in Clinical Translation of Antimicrobial Strategies for the Control of Biomaterial-Implant-Associated Infection

Current regulatory requirements impede clinical translation and market introduction of many new antimicrobial combination implants and devices, causing unnecessary patient suffering, doctor frustration, and costs to healthcare payers. Regulatory requirements of antimicrobial combination implants and devices should be thoroughly revisited and their approval allowed based on enrichment of benefit demonstrations from high-risk patient groups and populations or device components to facilitate their clinical translation. Biomaterial implant and devices equipped with antimicrobial strategies and approved based on enrichment claims should be mandatorily enrolled in global registry studies supervised by regulatory agencies for a minimum five-year period or until statistically validated evidence for noninferiority or superiority of claims is demonstrated. With these recommendations, this trans-Atlantic consortium of academicians and clinicians takes its responsibility to actively seek to relieve the factors that stagnate downward clinical translation and availability of antimicrobial combination implants and devices. Improved dialogue between the various key players involved in the current translational blockade, which include patients, academicians and doctors, policymakers, regulatory agencies, manufacturers, and healthcare payers, is urgently needed.</p

Video crowd detection and behavior analysis

Cette thèse porte sur la similitude entre un fluide et une foule et sur l'adaptation de l’algorithme de Particle Video pour le suivi et l'analyse de foule, ce qui aboutit à la conception d'un système complet pour l'analyse de la foule. Cette thèse en étudie trois aspects : la détection de la foule, l'estimation de sa densité et le tracking des flux afin d'obtenir des caractéristiques de comportement.L’algorithme de détection de la foule est une méthode totalement non supervisée pour la détection et la localisation des foules denses dans des images non-contextualisées. Après avoir calculé des vecteurs de features multi-échelles, une classification binaire est effectuée afin d'identifier la foule et l'arrière-plan.L'algorithme d'estimation de densité s'attaque au problème de l'apprentissage de modèles de régression dans le cas de larges foules denses. L'apprentissage est alors impossible sur données réelles car la vérité terrain est indisponible. Notre méthode repose donc sur l'utilisation de données synthétiques pour la phase d'apprentissage et prouve que le modèle de régression obtenu est valable sur données réelles.Pour notre adaptation de l’algorithme de Particle Video nous considérons le nuage de particules comme statistiquement représentatif de la foule. De ce fait, chaque particule possède des propriétés physiques qui nous permettent d'évaluer la validité de son comportement en fonction de celui attendu d'un piéton et d’optimiser son mouvement guidé par le flot optique. Trois applications en découlent : détection des zones d’entrée-sortie de la foule, détection des occlusions dynamiques et mise en relation des zones d'entrée et de sortie selon les flux de piétons.This thesis focuses on the similarity between a fluid and a crowd and on the adaptation of the particle video algorithm for crowd tracking and analysis. This interrogation ended up with the design of a complete system for crowd analysis out of which, this thesis has addressed three main problems: the detection of the crowd, the estimation of its density and the tracking of the flow in order to derive some behavior features.The contribution to crowd detection introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. After retrieving multi-scale texture-related feature vectors from the image, a binary classification is conducted to identify the crowd and the background.The density estimation algorithm is tackling the problem of learning regression models when it comes to large dense crowds. In such cases, the learning is impossible on real data as the ground truth is not available. Our method relies on the use of synthetic data for the learning phase and proves that the regression model obtained is valid for a use on real data.Our adaptation of the particle video algorithm leads us to consider the cloud of particles as statistically representative of the crowd. Therefore, each particle has physical properties that enable us to assess the validity of its behavior according to the one expected from a pedestrian, and to optimize its motion guided by the optical flow. This leads us to three applications: the detection of the entry and exit areas of the crowd in the image, the detection of dynamic occlusions and the possibility to link entry areas with exit ones, according to the flow of the pedestrians

Instructions sommaires théoriques et pratiques sur les Accouchemens

par Cecille Fagett

Unsupervised dense crowd detection by multiscale texture analysis

International audienceThis study introduces a totally unsupervised method for the detection and location of dense crowds in images without context-awareness. With the perspective of setting up fully autonomous video-surveillance systems, automatic detection and location of crowds is a crucial step that is going to point which areas of the image have to be analyzed. After retrieving multiscale texture-related feature vectors from the image, a binary classification is conducted to determine which parts of the image belong to the crowd and which to the background. The algorithm presented can be operated on images without any prior knowledge of any kind and is totally unsupervised

Dense, metric and real-time 3D reconstruction for autonomous drone navigation

The navigation of small drones within an unknown area requires perception and analysis of their surrounding environments, especially in GNSS-denied regions. A way to achieve this is by reconstructing a 3D dense metric map in real time. It allows the drone to localize itself, plan its trajectory and to be aware of potential static and dynamic obstacles. Visual Inertial Odometry (VIO) algorithms focus on robot localization and trajectory, while Simultaneous Localization and Mapping (SLAM) maintains both localization and mapping. These types of algorithms seem to have reached maturity, and now face new challenges related to real applications in robotics. Studies are now towards robustness and efficiency through new sensors and Deep Learning (DL)