Apprentissage incrémental pour la détection de chute de personnes âgées

International audienceDans ce papier, nous proposons une méthodologie d'évolution supervisée d'un modèle de classification, spécifique à un système de détection de chute de personnes mis au point précédemment. Cette méthodologie met en oeuvre la méthode de détection, un protocole d'apprentissage incrémental ou évolutif, et une méthode d'évaluation et de comparaison des performances, devant conduire à une amélioration des capacités de détection de chutes sur un système embarqué de type caméra intelligente

Détection automatique de chutes de personnes basée sur des descripteurs spatio-temporels (définition de la méthode, évaluation des performances et implantation temps-réel)

Nous proposons une méthode supervisée de détection de chutes de personnes en temps réel, robusteaux changements de point de vue et d environnement. La première partie consiste à rendredisponible en ligne une base de vidéos DSFD enregistrées dans quatre lieux différents et qui comporteun grand nombre d annotations manuelles propices aux comparaisons de méthodes. Nousavons aussi défini une métrique d évaluation qui permet d évaluer la méthode en s adaptant à la naturedu flux vidéo et la durée d une chute, et en tenant compte des contraintes temps réel. Dans unsecond temps, nous avons procédé à la construction et l évaluation des descripteurs spatio-temporelsSTHF, calculés à partir des attributs géométriques de la forme en mouvement dans la scène ainsique leurs transformations, pour définir le descripteur optimisé de chute après une méthode de sélectiond attributs. La robustesse aux changements d environnement a été évaluée en utilisant les SVMet le Boosting. On parvient à améliorer les performances par la mise à jour de l apprentissage parl intégration des vidéos sans chutes enregistrées dans l environnement définitif. Enfin, nous avonsréalisé, une implantation de ce détecteur sur un système embarqué assimilable à une caméra intelligentebasée sur un composant SoC de type Zynq. Une démarche de type Adéquation AlgorithmeArchitecture a permis d obtenir un bon compromis performance de classification/temps de traitementWe propose a supervised approach to detect falls in home environment adapted to location andpoint of view changes. First, we maid publicly available a realistic dataset, acquired in four differentlocations, containing a large number of manual annotation suitable for methods comparison. We alsodefined a new metric, adapted to real-time tasks, allowing to evaluate fall detection performance ina continuous video stream. Then, we build the initial spatio-temporal descriptor named STHF usingseveral combinations of transformations of geometrical features and an automatically optimised setof spatio-temporal descriptors thanks to an automatic feature selection step. We propose a realisticand pragmatic protocol which enables performance to be improved by updating the training in thecurrent location with normal activities records. Finally, we implemented the fall detection in Zynqbasedhardware platform similar to smart camera. An Algorithm-Architecture Adequacy step allowsa good trade-off between performance of classification and processing timeDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Solid pseudopapillary tumor of the pancreas: Ecadherin, β-catenin, CD99 new useful markers with characteristic expression (about two case reports)

Solid pseudopapillary neoplasm of the pancreas is a rare tumor that has favorable prognosis. It poses frequently diagnostic challenges. We describe two cases of solid pseudopapillary tumor of the pancreas managed in our department between 2007 and 2011. Two females have mean age of 36.5 years. Clinical presentation include: abdominal pain, bloating and palpable abdominal mass. Tumor is localized in the head of the pancreas in one case and in the tail in the other case. The mean size of the mass was 6 cm (range: 5 to 7 cm). Surgical treatment was performed in two cases. Histological examination confirms the diagnosis of solid pseudopapillary tumor of the pancreas. Immunohistochemical analysis was concordant to the literature data especially concerning CD99 which positivity was in dot, loss of positivity of E-cadherin and nuclear staining of β-catenin. CD10 and α-1-antitrypsin were also positive. One patient was dead 3 days postoperative and neither cancer recurrence nor distant metastases were detected on the follow up of the other. However, solid pseudo-papillary tumor of the pancreas has a distinctive histological appearance; some cases are problematic requiring the use of immunohistochemistry to distinguish it from other pancreatic neoplasm which prognosis is different

Automatic human fall detection based on spatio-temporal descriptors : definition of the method, evaluation of the performance and real-time implementation

Nous proposons une méthode supervisée de détection de chutes de personnes en temps réel, robusteaux changements de point de vue et d’environnement. La première partie consiste à rendredisponible en ligne une base de vidéos DSFD enregistrées dans quatre lieux différents et qui comporteun grand nombre d’annotations manuelles propices aux comparaisons de méthodes. Nousavons aussi défini une métrique d’évaluation qui permet d’évaluer la méthode en s’adaptant à la naturedu flux vidéo et la durée d’une chute, et en tenant compte des contraintes temps réel. Dans unsecond temps, nous avons procédé à la construction et l’évaluation des descripteurs spatio-temporelsSTHF, calculés à partir des attributs géométriques de la forme en mouvement dans la scène ainsique leurs transformations, pour définir le descripteur optimisé de chute après une méthode de sélectiond’attributs. La robustesse aux changements d’environnement a été évaluée en utilisant les SVMet le Boosting. On parvient à améliorer les performances par la mise à jour de l’apprentissage parl’intégration des vidéos sans chutes enregistrées dans l’environnement définitif. Enfin, nous avonsréalisé, une implantation de ce détecteur sur un système embarqué assimilable à une caméra intelligentebasée sur un composant SoC de type Zynq. Une démarche de type Adéquation AlgorithmeArchitecture a permis d’obtenir un bon compromis performance de classification/temps de traitementWe propose a supervised approach to detect falls in home environment adapted to location andpoint of view changes. First, we maid publicly available a realistic dataset, acquired in four differentlocations, containing a large number of manual annotation suitable for methods comparison. We alsodefined a new metric, adapted to real-time tasks, allowing to evaluate fall detection performance ina continuous video stream. Then, we build the initial spatio-temporal descriptor named STHF usingseveral combinations of transformations of geometrical features and an automatically optimised setof spatio-temporal descriptors thanks to an automatic feature selection step. We propose a realisticand pragmatic protocol which enables performance to be improved by updating the training in thecurrent location with normal activities records. Finally, we implemented the fall detection in Zynqbasedhardware platform similar to smart camera. An Algorithm-Architecture Adequacy step allowsa good trade-off between performance of classification and processing tim

Détection automatique de chutes de personnes basée sur des descripteurs spatio-temporels : définition de la méthode, évaluation des performances et implantation temps-réel

We propose a supervised approach to detect falls in home environment adapted to location andpoint of view changes. First, we maid publicly available a realistic dataset, acquired in four differentlocations, containing a large number of manual annotation suitable for methods comparison. We alsodefined a new metric, adapted to real-time tasks, allowing to evaluate fall detection performance ina continuous video stream. Then, we build the initial spatio-temporal descriptor named STHF usingseveral combinations of transformations of geometrical features and an automatically optimised setof spatio-temporal descriptors thanks to an automatic feature selection step. We propose a realisticand pragmatic protocol which enables performance to be improved by updating the training in thecurrent location with normal activities records. Finally, we implemented the fall detection in Zynqbasedhardware platform similar to smart camera. An Algorithm-Architecture Adequacy step allowsa good trade-off between performance of classification and processing timeNous proposons une méthode supervisée de détection de chutes de personnes en temps réel, robusteaux changements de point de vue et d’environnement. La première partie consiste à rendredisponible en ligne une base de vidéos DSFD enregistrées dans quatre lieux différents et qui comporteun grand nombre d’annotations manuelles propices aux comparaisons de méthodes. Nousavons aussi défini une métrique d’évaluation qui permet d’évaluer la méthode en s’adaptant à la naturedu flux vidéo et la durée d’une chute, et en tenant compte des contraintes temps réel. Dans unsecond temps, nous avons procédé à la construction et l’évaluation des descripteurs spatio-temporelsSTHF, calculés à partir des attributs géométriques de la forme en mouvement dans la scène ainsique leurs transformations, pour définir le descripteur optimisé de chute après une méthode de sélectiond’attributs. La robustesse aux changements d’environnement a été évaluée en utilisant les SVMet le Boosting. On parvient à améliorer les performances par la mise à jour de l’apprentissage parl’intégration des vidéos sans chutes enregistrées dans l’environnement définitif. Enfin, nous avonsréalisé, une implantation de ce détecteur sur un système embarqué assimilable à une caméra intelligentebasée sur un composant SoC de type Zynq. Une démarche de type Adéquation AlgorithmeArchitecture a permis d’obtenir un bon compromis performance de classification/temps de traitemen

Spatio-temporal descriptor for SVM and Adaboost based fall detection

International audienc

Definition and performance evaluation of a robust SVM based fall dectection system

International audienc

Optimised spatio-temporal descriptors for real-time fall detection : comparison of SVM and Adaboost based classification

International audienceWe propose a supervised approach to detect falls in home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing to evaluate fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, Wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vectormachine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activitiesrecords

Fast prototyping of a SoC-based smart camera:a real-time fall detection case study

International audienceSmart camera, i.e. cameras that are able to acquire and process images in real-time, is a typical example of the new embedded computer vision systems. A key example of application is automatic fall detection, which can be useful for helping elderly people in daily life. In this paper, we propose a methodology for development and fast-prototyping of a fall detection system based on such a smart camera, which allows to reduce the development time compared to standard approaches. Founded on a supervised classification approach, we propose a HW/SW implementation to detect falls in a home environment using a single camera and an optimized descriptor adapted to real-time tasks. This heterogeneous implementation is based on Xilinx’s system-on-chip named Zynq. The main contributions of this work are (i) the proposal of a codesignmethodology. These methodologies enable the HW/SW partitioning to be delayed using high-level algorithmic description and high-level synthesis tools. Our approach enables fast prototyping which allows fast architecture exploration and optimisation to be performed, (ii) the design of a hardware accelerator dedicated to boostingbased classification, which is a very popular and efficient algorithm used in image analysis, (iii) the proposal of falldetection embedded in a smart camera and enabling integration into the elderly people environment. Performances of our system are finally compared to the state-of-the-art

Video Scene analysis for a configurable hardware accelerator dedicated to Smart Camera

International audienceAccording to the Center for Research and Prevention of Injuries report, fall-caused injuries of elderly people in UE- 27 are five times as frequent as other injury causes which reduce considerably their mobility and independence. Among the diverse applications of computer vision systems, object detection and event recognition are of the most prominent related recognition and motion analysis, that is, researchers had the idea to spread it in fall detection. The fall event, extracted automatically from the video scene represents itself, crucial information that can be used to alert emergency. In this context, visual information on the corresponding scene is highly important in order to take the "right" decision. Therefore, video compression may be included into the acquisition system to reduce data-bandwidth. Meanwhile, detecting such particular situations allows the video compression to be controlled. For instance, the compression can be reduced after a fall to provide more details on the scene or specific compression rates can be applied on the background and the regions of interest. The video codec should be able to adjust its coding performances (bit-rate, PSNR) according to the detected events into the video scene. For reaching such flexibility, we propose to focus on a key processing stage: the motion estimation. Ideally, a smart camera which embedded a fall detection method as well as an adaptive video codec would be very significant solution for this field. We propose two key elements for the design of such system: a robust fall detection method and a configurable motion estimation accelerator. The fall detection method is based on basic feature extraction (bounding box aspect ratio and position best fitting ellipse characteristics, etc.) from robust tracking algorithm, followed by a SVM classifier and a final decision stage. We evaluated the robustness of our method using a realistic dataset and we evaluated the ability of some standard transformations to improve the classification performances. Experiments show that the best result (specificify=0.99984, accuracy=0.99822, precision = 0.94737 and recall=0.9), is obtained combining the Fast Fourier Transform, the Wavelet transform and the first derivative, i.e. the velocity of the first level feature. The hardware implementation of the motion estimator which enables the Integer Motion Estimation (IME) algorithms to be modified and the Fractional Motion Estimation (FME) and Variable Block Size (VBS) to be selected and adjusted according the performances to be reached. This low-cost implementation enables to reach high-speed performances. Using a Diamond Search (DS) method for IME stage, a 1080 HD (1920x1088) video stream can be processed up to 223 fps. Moreover for FME mode, same video streams can be processed at frame rate of 29 fps at 250 MHz (around 232K macroblocks/s). We plan in the next future to improve the robustness embedding the training system, allowing the people to update easily the SVM model, in order to take into account the specificities of the final home-user