Les manifestations violentes

Abstract. An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transferable Belief Model (TBM) framework for action recognition. The TBMbased modelling and fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. Action recognition is performed by a multilevel analysis. The sequencing is exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of jumps: high jump, pole vault, triple jump and long jump.

Ein Neues Verfahren zur Messung der Bakteriziden Fähigkeit des Vollblutes

Das oben erwahnte Verfahren hat vor den anderen Methoden besonders die Vorzuge, 1) da&#946; man dadurch zu einem sicheren Resultat gelangen und gleichzeitig auch jedes Datum mit exakten Ziffern zum Ausdruck bringen kann, 2) da&#946; bei diesem Verfahren keineswegs erforderlich ist, eine bestimmte Anzahl von Keimen einschlie&#946;ende Bakterienaufschwemmung herzusteHen und auch Kontrollversuch anzustellen, 3) da&#946; es von den Fehlern des Mischverhaltnisses zwischen der Bakterienlosung und dem Blut nicht so erheblich beeinflu&#946;t wird, und 4) da&#946; man durch dieses Verfahren gleichzeitig mehrere bakterientotende Faktoren untersuchen kann. Ferner hat dieses Verfahren auch den Vorzug, da&#946; es praktisch sehr einfach auszufuhren ist und nur 6 Stunden nach der Blutentnahme bereits das Ergebnis liefert. Es gestattet ferner, die bakterizide Kraft des Blutes gleichzeitig bei 6 - 8 Menschen zu untersuchen, was mich zur Uberzeugung fuhrt, da&#946; es in der Klinik hochgeschatzt werden wird. Auch das Verfahren und die ebenfalls vom mir aufgestellte Formel zur zusammenfassenden Beurteilung kann man nach meinem Erachten durch entsprechende Veranderungen einiger Faktoren ohne jede Schwierigkeiten auch fur andere Bakterienarten anwenden. Man wird wohl gegen eine einzige Lucke dieses Verfahrens, da&#946; die mikroskopische Untersuchung und die Berechnung allzu verwickelt zu sein scheint, Einwand erheben, eine Lucke, zu deren Schlu&#946; jedoch nur eine kurzfristige Ubung erfordert wird, durch welche die mikroskopische Untersuchung innerhalb 30 Minuten, die Berechnung nur in 5 Minuten vollendet werden kann. (Zur Berechnung bedarf es einer Gauss'schen Logarithmentafel.) Obgleich das geschilderte Verfahren noch viele, genauere Prufungen erheischende Punkte in sich einschlie&#946;t, mu&#946; es hier, wenn auch in Grundzugen, jetzt schon angefuhrt werden,. da ich der festen Uberzeugung bin, da&#946; es im Vergleich zu den bisherigen Methoden ein dem wirklichen Wert der Bakterizidie des Vollblutes im lebenden Organismus viel naheres Resultat liefert.</p

Camera motion estimation through planar deformation determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion, that allows to separate the image deformation in a similarity and a ``purely'' projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediatly deduce camera motion parameters.Comment: 21 pages, version modifi\'ee accept\'e le 20 mars 200

Tracking the Multi Person Wandering Visual Focus of Attention

Estimating the {\em wandering visual focus of attention} (WVFOA) for multiple people is an important problem with many applications in human behavior understanding. One such application, addressed in this paper, monitors the attention of passers-by to outdoor advertisements. To solve the WVFOA problem, we propose a multi-person tracking approach based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in the scene, their body and head locations, and their head pose, in a joint state-space formulation that is amenable for person interaction modeling. The model exploits both global measurements and individual observations for the VFOA. For inference in the resulting high-dimensional state-space, we propose a trans-dimensional Markov Chain Monte Carlo (MCMC) sampling scheme, which not only handles a varying number of people, but also efficiently searches the state-space by allowing person-part state updates. Our model was rigorously evaluated for tracking and its ability to recognize when people look at an outdoor advertisement using a realistic data set

Tracking Attention for Multiple People: Wandering Visual Focus of Attention Estimation

The problem of finding the visual focus of attention of multiple people free to move in an unconstrained manner is defined here as the {\em wandering visual focus of attention} (WVFOA) problem. Estimating the WVFOA for multiple unconstrained people is a new and important problem with implications for human behavior understanding and cognitive science, as well as real-world applications. One such application, which we present in this article, monitors the attention passers-by pay to an outdoor advertisement. In our approach to the WVFOA problem, we propose a multi-person tracking solution based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. It is defined in a joint state-space formulation that allows for the modeling of interactions between people. For inference in the resulting high-dimensional state-space, we propose a trans-dimensional Markov Chain Monte Carlo (MCMC) sampling scheme, which not only handles a varying number of people, but also efficiently searches the state-space by allowing person-part state updates. Our model was rigorously evaluated for tracking quality and ability to recognize people looking at an outdoor advertisement, and the results indicate good performance for these tasks

Tracking the visual focus of attention for a varying number of wandering people

In this article, we define and address the problem of finding the visual focus of attention for a varying number of wandering people (VFOA-W) -- determining where a person is looking when their movement is unconstrained. VFOA-W estimation is a new and important problem with implications in behavior understanding and cognitive science, as well as real-world applications. One such application, presented in this article, monitors the attention passers-by pay to an outdoor advertisement using a single video camera. In our approach to the VFOA-W problem, we propose a multi-person tracking solution based on a dynamic Bayesian network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. For efficient inference in the resulting variable-dimensional state-space we propose a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampling scheme, as well as a novel global observation model which determines the number of people in the scene and their locations. To determine if a person is looking at the advertisement or not, we propose a Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM)-based VFOA-W model which uses head pose and location information. Our models are evaluated for tracking performance and ability to recognize people looking at an outdoor advertisement, with results indicating good performance on sequences where up to three people pass in front of an advertisement

Tracking the Multi Person Wandering Visual Focus of Attention

Estimating the {\em wandering visual focus of attention} (WVFOA) for multiple people is an important problem with many applications in human behavior understanding. One such application, addressed in this paper, monitors the attention of passers-by to outdoor advertisements. To solve the WVFOA problem, we propose a multi-person tracking approach based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in the scene, their body and head locations, and their head pose, in a joint state-space formulation that is amenable for person interaction modeling. The model exploits both global measurements and individual observations for the VFOA. For inference in the resulting high-dimensional state-space, we propose a trans-dimensional Markov Chain Monte Carlo (MCMC) sampling scheme, which not only handles a varying number of people, but also efficiently searches the state-space by allowing person-part state updates. Our model was rigorously evaluated for tracking and its ability to recognize when people look at an outdoor advertisement using a realistic data set

Understanding Metro Station Usage using Closed Circuit Television Cameras Analysis

In this paper, we propose to show how video data available in standard CCTV transportation systems can represent a useful source of information for transportation infrastructure management, optimization and planning if adequately analyzed (e.g. to facilitate equipment usage understanding, to ease diagnostic and planning for system managers). More precisely, we present two algorithms allowing to estimate the number of people in a camera view and to measure the platform time-occupancy by trains. A statistical analysis of the results of each algorithm provide interesting insights regarding station usage. It is also shown that combining information from the algorithms in different views provide a finer understanding of the station usage. An end-user point of view confirms the interest of the proposed analysis

Stressful first impressions in job interviews

Stress can impact many aspects of our lives, such as the way we interact and work with others, or the first impressions that we make. In the past, stress has been most commonly assessed through self-reported questionnaires; however, advancements in wearable technology have enabled the measurement of physiological symptoms of stress in an unobtrusive manner. Using a dataset of job interviews, we investigate whether first impressions of stress (from annotations) are equivalent to physiological measurements of the electrodermal activity (EDA). We examine the use of automatically extracted nonverbal cues stemming from both the visual and audio modalities, as well EDA stress measurements for the inference of stress impressions obtained from manual annotations. Stress impressions were found to be significantly negatively correlated with hireability ratings i.e individuals who were perceived to be more stressed were more likely to obtained lower hireability scores. The analysis revealed a significant relationship between audio and visual features but low predictability and no significant effects were found for the EDA features. While some nonverbal cues were more clearly related to stress, the physiological cues were less reliable and warrant further investigation into the use of wearable sensors for stress detection