Abstract-Automatic detection of a falling person in video sequences has interesting applications in video -surveillance and is an important part of future pervasive home monitoring systems. In this paper, we propose a multiview approach to achieve this goal, where motion is modeled using a layered hidden Markov model (LHMM). The posture classification is performed by a fusion unit, merging the decision provided by the independently processing cameras in a fuzzy logic context. In each view, the fall detection is optimized in a given plane by performing a metric image rectification, making it possible to extract simple and robust features, and being convenient for real-time purpose. A theoretical analysis of the chosen descriptor enables us to define the optimal camera placement for detecting people falling in unspecified situations, and we prove that two cameras are sufficient in practice. Regarding event detection, the LHMM offers a principle way for solving the inference problem. Moreover, the hierarchical architecture decouples the motion analysis into different temporal granularity levels, making the algorithm able to detect very sudden changes, and robust to low-level steps errors.Index Terms-Fall detection, layered hidden Markov model (LHMM), metric rectification, multiview pose classification.

Abstract-This paper addresses the problem of automatic audio analysis for aided surveillance application in public transport. The aim of such application is to detect critical situations and to warn the control room. We propose a comparative study of two methods of modelisation/classification of acoustical segments. The problem is quite similar to the "'audio indexing"' framework, nevertheless the environment here is very noisy. We present two general frameworks based on Gaussian Model Mixture (GMM) and Support Vector Machine (SVM) to achieve shout detection in railway embedded environment.

A precise GNSS (Global Navigation Satellite System) localization is vital for autonomous road vehicles, especially in cluttered or urban environments where satellites are occluded, preventing accurate positioning. We propose to fuse GPS (Global Positioning System) data with fisheye stereovision to face this problem independently to additional data, possibly outdated, unavailable, and needing correlation with reality. Our stereoscope is sky-facing with 360° × 180° fisheye cameras to observe surrounding obstacles. We propose a 3D modelling and plane extraction through following steps: stereoscope self-calibration for decalibration robustness, stereo matching considering neighbours epipolar curves to compute 3D, and robust plane fitting based on generated cartography and Hough transform. We use these 3D data with GPS raw data to estimate NLOS (Non Line Of Sight) reflected signals pseudorange delay. We exploit extracted planes to build a visibility mask for NLOS detection. A simplified 3D canyon model allows to compute reflections pseudorange delays. In the end, GPS positioning is computed considering corrected pseudoranges. With experimentations on real fixed scenes, we show generated 3D models reaching metric accuracy and improvement of horizontal GPS positioning accuracy by more than 50%. The proposed procedure is effective, and the proposed NLOS detection outperforms CN0-based methods (Carrier-to-receiver Noise density).

For several years road vehicle autonomous cruise control (ACC) systems as well as anti-collision radar have been developed. Several manufacturers currently sell this equipment. The current generation of ACC sensors only track the first preceding vehicle to deduce its speed and position. These data are then used to compute, manage and optimize a safety distance between vehicles, thus providing some assistance to car drivers. However, in real conditions, to elaborate and update a real time driving solution, car drivers use information about speed and position of preceding and following vehicles. This information is essentially perceived using the driver's eyes, binocular stereoscopic vision performed through the windscreens and rear-view mirrors. Furthermore, within a line of vehicles, the frontal road perception of the first vehicle is very particular and highly significant. Currently, all these available data remain strictly on-board the vehicle that has captured the perception information and performed these measurements. To get the maximum effectiveness of all these approaches, we propose that this information be shared in real time with the following vehicles, within the convoy. On the basis of these considerations, this paper technically explores a cost-effective solution to extend the basic ACC sensor function in order to simultaneously provide a vehicle-to-vehicle radio link. This millimetre wave radio link transmits relevant broadband perception data (video, localization . . . ) to following vehicles, along the line of vehicles. The propagation path between the vehicles uses essentially grazing angles of incidence of signals over the road surface including millimetre wave paths beneath the cars.

The Global System for Mobile communications— 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

This paper presents an audio-video surveillance system for the automatic surveillance in public transport vehicle. The system comprises six modules including in particular three novel ones: (i) Face Detection and Tracking, (ii) Audio Event Detection and (iii) Audio-Video Scenario Recognition. The Face Detection and Tracking module is responsible for detecting and tracking faces of people in front of cameras. The Audio Event Detection module detects abnormal audio events which are precursor for detecting scenarios which have been predefined by end-users. The Audio-Video Scenario Recognition module performs high level interpretation of the observed objects by combining audio and video events based on spatio-temporal reasoning. The performance of the system is evaluated for a series of pre-defined audio, video and audio-video events specified using an audio-video event ontology.

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