Capteur stéréoscopique mono caméra pour la reconstruction 3D

International audienc

Long distance vision sensor for driver assistance

International audienc

Capteur stéréoscopique mono caméra pour la reconstruction 3D

International audienc

Human Action Recognition from Body-Part Directional Velocity using Hidden Markov Models

International audienceThis paper introduces a novel approach for early recognition of human actions using 3D skeleton joints extracted from 3D depth data. We propose a novel, frame-by-frame and real-time descriptor called Body-part Directional Velocity (BDV) calculated by considering the algebraic velocity produced by different body-parts. A real-time Hidden Markov Models algorithm with Gaussian Mixture Models state-output distributions is used to carry out the classification. We show that our method outperforms various state-of-the-art skeleton-based human action recognition approaches on MSRAction3D and Florence3D datasets. We also proved the suitability of our approach for early human action recognition by deducing the decision from a partial analysis of the sequence

Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction

Anomaly detection in surveillance videos is the identification of rare events which produce different features from normal events. In this paper, we present a survey about the progress of anomaly detection techniques and introduce our proposed framework to tackle this very challenging objective. Our approach is based on the more recent state-of-the-art techniques and casts anomalous events as unexpected events in future frames. Our framework is so flexible that you can replace almost important modules by existing state-of-the-art methods. The most popular solutions only use future predicted informations as constraints for training a convolutional encode-decode network to reconstruct frames and take the score of the difference between both original and reconstructed information. We propose a fully future prediction based framework that directly defines the feature as the difference between both future predictions and ground truth informations. This feature can be fed into various types of learning model to assign anomaly label. We present our experimental plan and argue that our framework's performance will be competitive with state-of-the art scores by presenting early promising results in feature extraction

Horloges atomiques microondes et optiques à microcellule

National audienceNous présentons en premier lieu des travaux visant au développement d’horloges atomiques microondes à microcellule de haute stabilité, basées sur le phénomène de piégeage cohérent de population (CPT). Nous démontrons l’implémentation de séquences d’interrogation impulsionnelles avancées permettant une réduction drastique des effets de déplacement lumineux, combinés à la poursuite d’efforts technologiques pour l’aboutissement d’une technologie de microcellule avancée. En second lieu, nous démontrons des références de fréquence optiques, lasers stabilisés sur microcellule par spectroscopie sub-Doppler bi-fréquence, présentant une stabilité relative de fréquence court terme de 1.5 10-12 à 1 s, 100 fois supérieure à celle des micro-horloges atomiques commerciales actuelles

Classification of Transient EM Noises Depending on their Effect on the Quality of GSM-R Reception

The Global System for Mobile communications\u2014 Railways (GSM-R) is being deployed in different countries to develop an efficient communication-based train control (CBTC) system. GSM-R participates to achieve railways interoperability, replacing noninteroperable CBTC on existing networks and, thus, facilitating cross-border train circulations. GSM-R ensures voice and data transmissions between trains and control centers and also between trains. As any radio equipment, it is subject to electro- magnetic (EM) disturbances present in the railway environment. Therefore, the quality of GSM-R transmissions can deteriorate. It is then important to evaluate and predict the effect of these dis- turbances in order to avoid any loss of train operational capacity. After an overview of the methods used for the characterization of the EM environment, we describe the GSM-R and the EM distur- bances that can affect its operation. The reasons why the existing characterization methods are not fully adapted to the GSM-R are highlighted. The general principle of classification is briefly re- called. The rest of this paper develops the methodology proposed to perform the classification of transient EM noises and the pre- sentation of a test bench and its associated experimental results. Finally, an application to an add-on electromagnetic compatibility supervising equipment installed on board the train is described