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186 results on '"Laure Tougne"'

1. Perimeter Intrusion Detection by Video Surveillance: A Survey

Author

Devashish Lohani, Carlos Crispim-Junior, Quentin Barthélemy, Sarah Bertrand, Lionel Robinault, and Laure Tougne Rodet

Subjects
perimeter intrusion detection, video surveillance, outdoor environment, real-time analysis, i-LIDS, Chemical technology, TP1-1185
Abstract

In recent times, we have seen a massive rise in vision-based applications, such as video anomaly detection, motion detection, object tracking, people counting, etc. Most of these tasks are well defined, with a clear idea of the goal, along with proper datasets and evaluation procedures. However, perimeter intrusion detection (PID), which is one of the major tasks in visual surveillance, still needs to be formally defined. A perimeter intrusion detection system (PIDS) aims to detect the presence of an unauthorized object in a protected outdoor site during a certain time. Existing works vaguely define a PIDS, and this has a direct impact on the evaluation of methods. In this paper, we mathematically define it. We review the existing methods, datasets and evaluation protocols based on this definition. Furthermore, we provide a suitable evaluation protocol for real-life application. Finally, we evaluate the existing systems on available datasets using different evaluation schemes and metrics.

Published
2022
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2. Registering Unmanned Aerial Vehicle Videos in the Long Term

Author

Pierre Lemaire, Carlos Fernando Crispim-Junior, Lionel Robinault, and Laure Tougne

Subjects
registration, stabilization, unmanned aerial vehicle, drone, Chemical technology, TP1-1185
Abstract

Unmanned aerial vehicles (UAVs) have become a very popular way of acquiring video within contexts such as remote data acquisition or surveillance. Unfortunately, their viewpoint is often unstable, which tends to impact the automatic processing of their video flux negatively. To counteract the effects of an inconsistent viewpoint, two video processing strategies are classically adopted, namely registration and stabilization, which tend to be affected by distinct issues, namely jitter and drifting. Following our prior work, we suggest that the motion estimators used in both situations can be modeled to take into account their inherent errors. By acknowledging that drifting and jittery errors are of a different nature, we propose a combination that is able to limit their influence and build a hybrid solution for jitter-free video registration. In this work, however, our modeling was restricted to 2D-rigid transforms, which are rather limited in the case of airborne videos. In the present paper, we extend and refine the theoretical ground of our previous work. This addition allows us to show how to practically adapt our previous work to perspective transforms, which our study shows to be much more accurate for this problem. A lightweight implementation enables us to automatically register stationary UAV videos in real time. Our evaluation includes traffic surveillance recordings of up to 2 h and shows the potential of the proposed approach when paired with background subtraction tasks.

Published
2021
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3. AutoExp: A multidisciplinary, multi-sensor framework to evaluate human activities in self-driving cars

Author

Crispim-Junior, Carlos, Guesdon, Romain, Jallais, Christophe, Laroche, Florent, Corvec, Stephanie Souche-Le, and Rodet, Laure Tougne

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The adoption of self-driving cars will certainly revolutionize our lives, even though they may take more time to become fully autonomous than initially predicted. The first vehicles are already present in certain cities of the world, as part of experimental robot-taxi services. However, most existing studies focus on the navigation part of such vehicles. We currently miss methods, datasets, and studies to assess the in-cabin human component of the adoption of such technology in real-world conditions. This paper proposes an experimental framework to study the activities of occupants of self-driving cars using a multidisciplinary approach (computer vision associated with human and social sciences), particularly non-driving related activities. The framework is composed of an experimentation scenario, and a data acquisition module. We seek firstly to capture real-world data about the usage of the vehicle in the nearest possible, real-world conditions, and secondly to create a dataset containing in-cabin human activities to foster the development and evaluation of computer vision algorithms. The acquisition module records multiple views of the front seats of the vehicle (Intel RGB-D and GoPro cameras); in addition to survey data about the internal states and attitudes of participants towards this type of vehicle before, during, and after the experimentation. We evaluated the proposed framework with the realization of real-world experimentation with 30 participants (1 hour each) to study the acceptance of SDCs of SAE level 4., Comment: This paper is currently under review by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)

Published
2023

28. Development of an indicator to assess past agricultural pesticides exposure in chronic diseases: application to the TESTIS epidemiological study.

Author

Grassot, Lény, Ratajczak, Rémi, Jouffroy, Léopold, Sueur, Annabelle, Dubuis, Matthieu, Crispim-Junior, Carlos, Faure, Elodie, Rodet, Laure Tougne, Fervers, Béatrice, and Coste, Astrid

Subjects
TESTICULAR diseases, AGRICULTURE, CHRONIC diseases, PESTICIDES, GONADS
Abstract

Copyright of Environnement, Risques & Santé is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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