A framework dealing with Uncertainty for Complex Event Recognition

International audienceThis paper presents a constraint-based approach for video complex event recognition with probabilistic reasoning for handling uncertainty. The main advantage of constraint-based approaches is the possibility for human expert to model composite events with complex temporal constraints. But the approaches are usually deterministic and do not enable the convenient mechanism of probability reasoning to handle the uncertainty. The first advantage of the proposed approach is the ability to model and recognize a large amount of composite events with complex temporal constraints. The second advantage is that probability theory provides a consistent framework for dealing with uncertain knowledge for a robust and reliable recognition of complex event. This approach is evaluated with 4 real healthcare videos and a public video database ETISEO'06. The results are compared with state of the art method. The comparison shows that the proposed approach improves significantly the process of recognition and characterizes the likelihood of the recognized events

Combining Multiple Sensors for Event Detection of Older People

International audienceWe herein present a hierarchical model-based framework for event detection using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a video monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty lies on combining multiple sensors at decision (event) level, and handling their conflict using a proba-bilistic approach. The event conflict handling consists of computing the reliability of each sensor before their fusion using an alternative combination rule for Dempster-Shafer Theory. The framework evaluation is performed on multisensor recording of instrumental activities of daily living (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease study. Two fusion cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) following a deterministic fashion, and the combination of conflicting events from video cameras with partially overlapped field of view (a RGB-and a RGB-D-camera, Kinect). Results showed the framework improves the event detection rate in both cases

Activity Recognition and Uncertain Knowledge in Video Scenes

International audienceActivity recognition has been a growing research topic in the last years and its application varies from auto-matic recognition of social interaction such as shaking hands, parking lot surveillance, traffic monitoring and the detection of abandoned luggage. This paper describes a probabilistic framework for uncertainty handling in a description-based event recognition approach. The proposed approach allows the flexible modeling of composite events with complex temporal constraints. It uses probability theory to provide a consistent framework for dealing with uncertain knowledge for the recognition of complex events. We validate the event recognition accuracy of the proposed algorithm on real-world videos. The experimental results show that our system can successfully recognize activities with a high recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of real-time event recognitio

Combining Multiple Sensors for Event Detection of Older People

International audienceWe herein present a hierarchical model-based framework for event detection using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a video monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty lies on combining multiple sensors at decision (event) level, and handling their conflict using a proba-bilistic approach. The event conflict handling consists of computing the reliability of each sensor before their fusion using an alternative combination rule for Dempster-Shafer Theory. The framework evaluation is performed on multisensor recording of instrumental activities of daily living (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease study. Two fusion cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) following a deterministic fashion, and the combination of conflicting events from video cameras with partially overlapped field of view (a RGB-and a RGB-D-camera, Kinect). Results showed the framework improves the event detection rate in both cases

Combining Multiple Sensors for Event Recognition of Older People

MIRRH, held in conjunction with ACM MM 2013.International audienceWe herein present a hierarchical model-based framework for event recognition using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipments) with moving objects (e.g., a Person) detected by a monitoring system. The event models follow a generic ontology based on natural language; which allows domain experts to easily adapt them. The framework novelty relies on combining multiple sensors (heterogeneous and homogeneous) at decision level explicitly or implicitly by handling their conflict using a probabilistic approach. The implicit event conflict handling works by computing the event reliabilities for each sensor, and then combine them using Dempster-Shafer Theory. The multi-sensor system is evaluated using multi-modal recording of instrumental daily living activities (e.g., watching TV, writing a check, preparing tea, organizing the week intake of prescribed medication) of participants of a clinical study of Alzheimer's disease. The evaluation presents the preliminary results of this approach on two cases: the combination of events from heterogeneous sensors (a RGB camera and a wearable inertial sensor); and the combination of conflicting events from video cameras with a partially overlapped field of view (a RGB- and a RGB-D-camera). The results show the framework improves the event recognition rate in both cases

Evaluation of a Monitoring System for Event Recognition of Older People

International audiencePopulation aging has been motivating academic research and industry to develop technologies for the improvement of older people's quality of life, medical diagnosis, and support on frailty cases. Most of available research prototypes for older people monitoring focus on fall detection or gait analysis and rely on wearable, environmental, or video sensors. We present an evaluation of a research prototype of a video monitoring system for event recognition of older people. The prototype accuracy is evaluated for the recognition of physical tasks (e.g., Up and Go test) and instrumental activities of daily living (e.g., watching TV, writing a check) of participants of a clinical protocol for Alzheimer's disease study (29 participants). The prototype uses as input a 2D RGB camera, and its performance is compared to the use of a RGB-D camera. The experimentation results show the proposed approach has a competitive performance to the use of a RGB-D camera, even outperforming it on event recognition precision. The use of a 2D-camera is advantageous, as the camera field of view can be much larger and cover an entire room where at least a couple of RGB-D cameras would be necessary

Automatic Video Monitoring system for assessment of Alzheimer's Disease symptoms

International audienceIn order to fully capture the complexity of the behavioural, functioning and cognitive disturbances in Alzheimer Disease (AD) and related disorders information and communication techniques (ICT), could be of interest. This article presents using 3 clinical cases the feasibility results of an automatic video monitoring system aiming to assess subjects involved in a clinical scenario

Video Activity Recognition Framework for assessing motor behavioural disorders in Alzheimer Disease Patients

International audiencePatients with Alzheimers disease show cognitive decline commonly associated with psycho-behavioural disorders like depression, apathy and motor behaviour disturbances. However current evaluations of psycho-behavioural disorders are based on interviews and battery of neuropsychological tests with the presence of a clinician. So these evaluations show limits of subjectivity (e.g., subjective interpretation of clinician at a date t). In this work, we study the ability of a proposed automatic video activity recognition system to detect activity changes between elderly subjects with and without dementia during a clinical experimentation. A total of 28 volunteers (11 healthy elderly subjects, 17 Alzheimer's disease patients (AD)) participate to the experimentation. The proposed study shows that we could differentiate the two profiles of participants based on motor activity parameters, such as the walking speed, computed from the proposed automatic video activity recognition system. These primary results are promising and validating the interest of automatic analysis of video as an objective evaluation tool providing comparative results between participants and over the time

Automatic Video Monitoring system for assessment of Alzheimer's Disease symptoms

International audienceIn order to fully capture the complexity of the behavioural, functioning and cognitive disturbances in Alzheimer Disease (AD) and related disorders information and communication techniques (ICT), could be of interest. This article presents using 3 clinical cases the feasibility results of an automatic video monitoring system aiming to assess subjects involved in a clinical scenario

Reconnaissance d'activités et connaissances incertaines dans les scènes vidéos appliquées à la surveillance de personnes âgées.

This work deals with the problem of human activity recognition. It is greatly motivated by research in video activity understanding applied to the domain of health care monitoring. Research on activity recognition is receiving an increasing attention from the scientific community today. It is one of the most challenging problem in computer vision and artificial intelligence domains. The main goal of the current activity recognition research consists in recognizing and understanding short-term action and long-term complex activities. In this work, we propose two main contributions. The first contribution consists of an approach for video activity recognition that addresses the uncertainty management issues for accurate event detection. The second contribution consists in defining an ontology and a knowledge base for health care monitoring and in particular Alzheimer monitoring at hospital. The proposed activity recognition approach combines semantic modelling together with a probabilistic reasoning to cope with the errors of low-level detectors and to handle activity recognition uncertainty. The probabilistic recognition of activities is based on Bayesian probability theory which provides a consistent framework for dealing with uncertain knowledge. The proposed probabilistic constraint verification approach based on Gaussian probability model enforces the accuracy of the activity recognition algorithm. We work in close collaboration with clinicians to define an ontology and a knowledge base for Alzheimer monitoring at hospital. The defined ontology contains several concepts useful for health care. We also define a number of criteria which could be observed by camera sensors to allow detection of early symptoms of Alzheimer's disease. We validate the proposed algorithm on real-world videos. The experimental results show that the proposed activity recognition algorithm can successfully recognize activities with a high recognition rate. The obtained results for health care monitoring highlight the advantages of the use of the proposed approach as a support platform for clinicians to objectively measure patient performance and obtain a quantifiable assessment of instrumental activities of daily living and gait analysis.Cette thèse aborde le problème de la reconnaissance d'activités. Elle est fortement motivée par la recherche dans le domaine de la reconnaissance des activités vidéo appliquée au domaine de la surveillance de personnes âgées. Dans ce travail, nous proposons deux contributions principales. La première contribution consiste en une approche pour la reconnaissance d'activité vidéo avec gestion de l'incertitude pour une détection précise d'événements. La deuxième contribution consiste à définir une ontologie et une base de connaissances pour la surveillance dans le domaine de la santé et en particulier la surveillance à l'hôpital de patients atteints de la maladie d'Alzheimer. L'approche de reconnaissance d'activité proposée combine une modélisation sémantique avec un raisonnement probabiliste pour faire face aux erreurs des détecteurs de bas niveau et pour gérer l'incertitude de la reconnaissance d'activité. La reconnaissance probabiliste des activités est basée sur la théorie des probabilités bayésienne qui fournit un cadre cohérent pour traiter les connaissances incertaines. L'approche proposée pour la vérification probabiliste des contraintes spatiale et temporelle des activités est basée sur le modèle de probabilité gaussienne. Nous avons travaillé en étroite collaboration avec les cliniciens pour définir une ontologie et une base de connaissances pour la surveillance à l'hôpital de patients atteints de la maladie d'Alzheimer. L'ontologie définie contient plusieurs concepts utiles dans le domaine de la santé. Nous avons également défini un certain nombre de critères qui peuvent être observés par les caméras pour permettre la détection des premiers symptômes de la maladie d'Alzheimer. Nous avons validé l'algorithme proposé sur des vidéos réelles. Les résultats expérimentaux montrent que l'algorithme de reconnaissance d'activité proposé a réussi à reconnaitre les activités avec un taux élevé de reconnaissance. Les résultats obtenus pour la surveillance de patients atteints de la maladie d'Alzheimer mettent en évidence les avantages de l'utilisation de l'approche proposée comme une plate-forme de soutien pour les cliniciens pour mesurer objectivement les performances des patients et obtenir une évaluation quantifiable des activités de la vie quotidienne