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A person (: people or persons, depending on context) is a being who has certain capacities or attributes such as reason, morality, consciousness or self-consciousness, and being a part of a culturally established form of social relations such as kinship, ownership of property, or legal responsibility. The defining features of personhood and, consequently, what makes a person count as a person, differ widely among cultures and contexts. In addition to the question of personhood, of what makes a being count as a person to begin with, there are further questions about personal identity and self: both about what makes any particular person that particular person instead of another, and about what makes a person at one time the same person as they were or will be at another time despite any intervening changes. The plural form "people" is often used to refer to an entire nation or ethnic group (as in

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Human

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Raison

La raison est généralement considérée comme une faculté propre de l'esprit humain dont la mise en œuvre lui permet de créer des critères de vérité et d'erreur et d'atteindre ses objectifs. Elle repos

Phénoménologie (philosophie)

La phénoménologie (du grec , « ce qui apparaît », et , « étude ») est un courant de pensée du fondé par Edmund Husserl dans l'optique de faire de la philosophie une discipline empirique. Elle tire

PHYS-101(en): General physics : mechanics (English)

Students will learn the principles of mechanics to enable a better understanding of physical phenomena, such as the kinematics and dyamics of point masses and solid bodies. Students will acquire the capacity to quantitatively analyze these effects with the appropriate theoretical tools.

CS-523: Advanced topics on privacy enhancing technologies

This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evaluate the protection they provide.

BIO-455: Introduction to law and ethics

Le but du cours est de familiariser l'étudiant-e aux notions de base du droit et de l'éthique applicables à la recherche en STV et à son transfert en applications, et de lui fournir les éléments essentiels pour identifier les enjeux juridiques et éthiques dans sa future pratique professionnelle.

Multi-camera face detection and recognition applied to people tracking

Michail Zervos

This thesis describes the design and implementation of a framework that can track and identify multiple people in a crowded scene captured by multiple cameras. A people detector is initially employed to estimate the position of individuals. Those positions estimates are used by the face detector to prune the search space of possible face locations and minimize the false positives. A face classifier is employed to assign identities to the trajectories. Apart from recognizing the people in the scene, the face information is exploited by the tracker to minimize identity switches. Only sparse face recognitions are required to generate identity-preserving trajectories. Three face detectors are evaluated based on the project requirements. The face model of a person is described by Local Binary Pattern (histogram) features extracted from a number of patches of the face, captured by different cameras. The face model is shared between cameras meaning that one camera can recognize a face relying on patches captured by a different camera. Three classifiers are tested for the recognition task and an SVM is eventually employed. Due to the properties of the LBP, the recognition is robust to illumination changes and facial expressions. Also the SVM is trained from multiple views of the face of each person making the recognition also robust to pose changes. The system is integrated with two trackers, the state-of-the-art Multi-Commodity Network Flow tracker and a frame-by-frame Kalman tracker. We validate our method on two datasets generated for this purpose. The integration of face information with the people tracker demonstrates excellent performance and significantly improves the tracking results on crowded scenes, while providing the identities of the people in the scene.

2013

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WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection

Stéphane Bouquet, Tatjana Chavdarova, François Fleuret, Pascal Fua, Cijo Jose, Andrii Maksai

People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple synchronized cameras gives the opportunity to improve detection performance. In this paper, we present a new large-scale and high-resolution dataset. It has been captured with seven static cameras in a public open area, and unscripted dense groups of pedestrians standing and walking. Together with the camera frames, we provide an accurate joint (extrinsic and intrinsic) calibration, as well as 7 series of 400 annotated frames for detection at a rate of 2 frames per second. This results in over 40 000 bounding boxes delimiting every person present in the area of interest, for a total of more than 300 individuals. We provide a series of benchmark results using baseline algorithms published over the recent months for multi-view detection with deep neural networks, and trajectory estimation using a non-Markovian model.

IEEE2018

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Associating Audio-Visual Activity Cues in a Dominance Estimation Framework

Daniel Gatica-Perez, Yan Huang

We address the problem of both estimating the dominant person in a meeting from a single audio source and identifying them visually in a multi-camera setting. We use a speaker diarization algorithm to perform speaker segmentation and clustering, representing when they spoke. Using a greedy ordered audio-visual association algorithm, we investigate using the speaker clusters to find the corresponding person in one of the video channels. The difficulty of the problem is that firstly the speaker diarization output is noisy (e.g. for participants who speak little) and often produces an unequal number of clusters to true participants. Secondly, personal visual activity from natural upper torso motion, which can include highly deformable pose changes and perspective distortion, is computed through computationally efficient coarse features. Our results using almost 2 hours of audio-visual data from 4-participant meetings show a strong correlation between the estimated speaker diarization and visual activity features, enabling the identification of the most dominant person as a pair of audio-visual channels.

Idiap2008

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