3D Human Video Retrieval: from Pose to Motion Matching

International audience3D video retrieval is a challenging problem lying at the heart of many primary research areas in computer graphics and computer vision applications. In this paper, we present a new 3D human shape matching and motion retrieval framework. Our approach is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface and a local motion retrieval achieved after motion segmentation. Matching is performed by an efficient method which takes advantage of a compact EHC representation in open curve Shape Space and an elastic distance measure. Moreover, local 3D video retrieval is performed by dynamic time warping (DTW) algorithm in the feature space vectors. Experiments on both synthetic and real 3D human video sequences show that our approach provides an accurate shape similarity in video compared to the best state-of-the-art approaches. Finally, results on motion retrieval are promising and show the potential of this approach

Extremal Human Curves: a New Human Body Shape and Pose Descriptor

Shape and pose similarityInternational audienceAutomatic estimation of 3D shape similarity from video is a very important factor for human action analysis, but also a challenging task due to variations in body topology and the high dimensionality of the pose configuration space.We consider the problem of 3D shape similarity in 3D video sequence for different actors and motions. Most current approaches use conventional global features as a shape descriptor and define the shape similarity using L2 distance. However, such methods are limited to coarse representation and do not sufficiently reflect the pose similarity of human perception. In this paper, we present a novel 3D human pose descriptor called Extremal Human Curves (EHC), extracted from both the spatial and the topological dimensions of body surface. To compare tow shapes, we use an elastic metric in Shape Space between their descriptors, based on static features, and then perform temporal convolutions, thereby capturing the pose information encoded in multiple adjacent frames. We quantitatively analyze the effectiveness of our descriptors for both 3D shape similarity in video and content-based pose retrieval for static shape, and show that each one can contribute, sometimes substantially, to more reliable human shape and pose analysis. Experimental results are promising and show the robustness and accuracy of the proposed approach by comparing the recognition performance against several stateof- the-art methods

School-based intervention to promote healthy nutrition in Sousse, Tunisia

Introduction: Obesity among children is a major risk factor for chronic diseases. School interventions programs can represent a mean to implement healthy nutrition attitudes at early ages. Our objective was to evaluate the effects of a school intervention program to promote healthy nutrition among adolescents, in terms of knowledge, behaviors and intention. Methods: Quasi experimental study among urban students in Sousse, Tunisia with 2 groups, intervention and control. The intervention group had an interactive program integrated with school courses that promoted healthy nutrition habits. Both groups had a pre post evaluation. Results: 2200 students aged from 12 to 16 participated to the pre post evaluation. In the intervention group, there were significant changes form pre to post test in knowledge, intentions, and behaviors. In the control group, almost no significant changes were observed. Conclusion: School intervention programs can represent an interesting approach to promote healthy nutrition habits among adolescent

Accurate 3D Action Recognition using Learning on the Grassmann Manifold

International audienceIn this paper we address the problem of modelling and analyzing human motion by focusing on 3D body skeletons. Particularly, our intent is to represent skeletal motion in a geometric and efficient way, leading to an accurate action-recognition system. Here an action is represented by a dynamical system whose observability matrix is characterized as an element of a Grassmann manifold. To formulate our learning algorithm, we propose two distinct ideas: (1) In the first one we perform classification using a Truncated Wrapped Gaussian model, one for each class in its own tangent space. (2) In the second one we propose a novel learning algorithm that uses a vector representation formed by concatenating local coordinates in tangent spaces associated with different classes and training a linear SVM. %\cite{Turaga:2011:PAMI:ActionOnGrassman} We evaluate our approaches on three public 3D action datasets: MSR-action 3D, UT-kinect and UCF-kinect datasets; these datasets represent different kinds of challenges and together help provide an exhaustive evaluation. The results show that our approaches either match or exceed state-of-the-art performance reaching 91.21\% on MSR-action 3D, 97.91\% on UCF-kinect, and 88.5\% on UT-kinect. Finally, we evaluate the latency, i.e. the ability to recognize an action before its termination, of our approach and demonstrate improvements relative to other published approaches

Characterization of two polyvalent phages infecting Enterobacteriaceae

Bacteriophages display remarkable genetic diversity and host specificity. In this study, we explore phages infecting bacterial strains of the Enterobacteriaceae family because of their ability to infect related but distinct hosts. We isolated and characterized two novel virulent phages, SH6 and SH7, using a strain of Shigella flexneri as host bacterium. Morphological and genomic analyses revealed that phage SH6 belongs to the T1virus genus of the Siphoviridae family. Conversely, phage SH7 was classified in the T4virus genus of the Myoviridae family. Phage SH6 had a short latent period of 16 min and a burst size of 103 ± 16 PFU/infected cell while the phage SH7 latent period was 23 min with a much lower burst size of 26 ± 5 PFU/infected cell. Moreover, phage SH6 was sensitive to acidic conditions (pH < 5) while phage SH7 was stable from pH 3 to 11 for 1 hour. Of the 35 bacterial strains tested, SH6 infected its S. flexneri host strain and 8 strains of E. coli. Phage SH7 lysed additionally strains of E. coli O157:H7, Salmonella Paratyphi, and Shigella dysenteriae. The broader host ranges of these two phages as well as their microbiological properties suggest that they may be useful for controlling bacterial populations

Antibiotic resistance and virulence of faecal enterococci isolated from food-producing animals in Tunisia

Antimicrobial agents exert a selection pressure not only on pathogenic, but also on commensal bacteria of the intestinal tract of humans and animals. The aim of this work was to determine the occurrence of different enterococcal species and to analyse the prevalence of antimicrobial resistance and the mechanisms implicated, as well as the genetic diversity in enterococci recovered from faecal samples of food-producing animals (poultry, beef and sheep) in Tunisia. Antimicrobial resistance and the mechanisms implicated were studied in 87 enterococci recovered from 96 faecal samples from animals of Tunisian farms. Enterococcus faecium was the most prevalent species detected (46 %), followed by E. hirae (33.5 %). High percentages of resistance to erythromycin and tetracycline were found among our isolates, and lower percentages to aminoglycosides and ciprofloxacin were identified. Most of the tetracycline-resistant isolates carried the tet(M) and/or tet(L) genes. The erm(B) gene was detected in all erythromycin-resistant isolates. The ant(6)-Ia, aph(3)-Ia and aac(6)-aph(2) genes were detected in nine aminoglycoside-resistant isolates. Of our isolates, 11.5 % carried the gelE gene and exhibited gelatinase acitivity. The esp gene was detected in 10 % of our isolates and the hyl gene was not present in any isolate. The predominant species (E. faecium and E. hirae) showed a high genetic diversity by repetitive extragenic palindromic (REP)-PCR. Food animals might play a role in the spread through the food chain of enterococci with virulence and resistance traits to humans. © 2014 Springer-Verlag Berlin Heidelberg and the University of Milan

Approches géométriques pour l'analyse du mouvement humain en 3D : application à la reconnaissance d'action et à l’indexation

Dans le cadre de cette thèse, nous proposons des approches géométriques permettant d’analyser des mouvements humains à partir de données issues de capteurs 3D. Premièrement, nous abordons le problème de comparaison de poses et de mouvements dans des séquences contenant des modèles de corps humain en 3D. En introduisant un nouveau descripteur, appelé Extremal Human Curve (EHC), la forme du corps humain dans une pose donnée est décrite par une collection de courbes. Ces courbes extraites de la surface du maillage relient les points se situant aux extrémités du corps. Dans un formalisme Riemannien, chacune de ces courbes est considérée comme un point dans un espace de formes offrant la possibilité de les comparer. Par ailleurs, les actions sont modélisées par des trajectoires dans cet espace, où elles sont comparées en utilisant la déformation temporelle dynamique. Deuxièmement, nous proposons une approche de reconnaissance d’actions et de gestes à partir de vidéos produites par des capteurs de profondeur. A travers une modélisation géométrique, une séquence d’action est représentée par un système dynamique dont la matrice d’observabilité est caractérisée par un élément de la variété de Grassmann. Par conséquent, la reconnaissance d’actions est reformulée en un problème de classification de points sur cette variété. Ensuite, un nouvel algorithme d’apprentissage basé sur la notion d’espaces tangents est proposé afin d’améliorer le système de reconnaissance. Les résultats de notre approche, testés sur plusieurs bases de données, donnent des taux de reconnaissance de haute précision et de faible latence.In this thesis, we focus on the development of adequate geometric frameworks in order to model and compare accurately human motion acquired from 3D sensors. In the first framework, we address the problem of pose/motion retrieval in full 3D reconstructed sequences. The human shape representation is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface. It allows efficient shape to shape comparison taking benefits from Riemannian geometry in the open curve shape space. As each human pose represented by this descriptor is viewed as a point in the shape space, we propose to model the motion sequence by a trajectory on this space. Dynamic Time Warping in the feature vector space is then used to compare different motions. In the second framework, we propose a solution for action and gesture recognition from both skeleton and depth data acquired by low cost cameras such as Microsoft Kinect. The action sequence is represented by a dynamical system whose observability matrix is characterized as an element of a Grassmann manifold. Thus, recognition problem is reformulated as a point classification on this manifold. Here, a new learning algorithm based on the notion of tangent spaces is proposed to improve recognition task. Performances of our approach on several benchmarks show high recognition accuracy with low latency

Approches Géométriques pour l'Analyse du Mouvement Humain en 3D: Application à la Reconnaissance d'Action et l’Indexation

In this thesis, we focus on the development of adequate geometric frameworks in order to model and compare accurately human motion acquired from 3D sensors. In the first framework, we address the problem of pose/motion retrieval in full 3D reconstructed sequences. The human shape representation is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface. It allows efficient shape to shape comparison taking benefits from Riemannian geometry in the open curve shape space. As each human pose represented by this descriptor is viewed as a point in the shape space, we propose to model the motion sequence by a trajectory on this space. Dynamic Time Warping in the feature vector space is then used to compare different motions. In the second framework, we propose a solution for action and gesture recognition from both skeleton and depth data acquired by low cost cameras such as Microsoft Kinect. The action sequence is represented by a dynamical system whose observability matrix is characterized as an element of a Grassmann manifold. Thus, recognition problem is reformulated as a point classification on this manifold. Here, a new learning algorithm based on the notion of tangent spaces is proposed to improve recognition task. Performances of our approach on several benchmarks show high recognition accuracy with low latency.Dans le cadre de cette thèse, nous proposons des approches géométriques permettant d0analyser des mouvements humains à partir de données issues de capteurs 3D. Premièrement, nous abordons le problème de comparaison de poses et de mouvements dans des séquences contenant des modèles de corps humain en 3D. En introduisant un nouveau descripteur, appelé Extremal Hu- man Curve (EHC), la forme du corps humain dans une pose donnée est décrite par une collection de courbes. Ces courbes extraites de la surface du maillage relient les points se situant aux extrémités du corps. Dans un formalisme Riemannien, chacune de ces courbes est considérée comme un point dans un espace de formes offrant la possibilité de les comparer. Par ailleurs, les actions sont modélisées par des trajectoires dans cet espace, où elles sont comparées en utilisant la déformation temporelle dynamique. Deuxièmement, nous proposons une approche de reconnaissance d0actions et de gestes à partir de vidéos produites par des capteurs de profondeur. A travers une modélisation géométrique, une séquence d0action est représentée par un système dynamique dont la matrice d0observabilité est caractérisée par un élément de la variété de Grassmann. Par conséquent, la reconnaissance d0actions est reformulée en un problème de classification de points sur cette variété. Ensuite, un nouvel algo- rithme d0apprentissage basé sur la notion d0espaces tangents est proposé afin d0améliorer le système de reconnaissance. Les résultats de notre approche, testés sur plusieurs bases de données, donnent des taux de reconnaissance de haute précision et de faible latence

Approches géométriques pour l'analyse du mouvement humain en 3D : application à la reconnaissance d'action et à l’indexation

Dans le cadre de cette thèse, nous proposons des approches géométriques permettant d’analyser des mouvements humains à partir de données issues de capteurs 3D. Premièrement, nous abordons le problème de comparaison de poses et de mouvements dans des séquences contenant des modèles de corps humain en 3D. En introduisant un nouveau descripteur, appelé Extremal Human Curve (EHC), la forme du corps humain dans une pose donnée est décrite par une collection de courbes. Ces courbes extraites de la surface du maillage relient les points se situant aux extrémités du corps. Dans un formalisme Riemannien, chacune de ces courbes est considérée comme un point dans un espace de formes offrant la possibilité de les comparer. Par ailleurs, les actions sont modélisées par des trajectoires dans cet espace, où elles sont comparées en utilisant la déformation temporelle dynamique. Deuxièmement, nous proposons une approche de reconnaissance d’actions et de gestes à partir de vidéos produites par des capteurs de profondeur. A travers une modélisation géométrique, une séquence d’action est représentée par un système dynamique dont la matrice d’observabilité est caractérisée par un élément de la variété de Grassmann. Par conséquent, la reconnaissance d’actions est reformulée en un problème de classification de points sur cette variété. Ensuite, un nouvel algorithme d’apprentissage basé sur la notion d’espaces tangents est proposé afin d’améliorer le système de reconnaissance. Les résultats de notre approche, testés sur plusieurs bases de données, donnent des taux de reconnaissance de haute précision et de faible latence.In this thesis, we focus on the development of adequate geometric frameworks in order to model and compare accurately human motion acquired from 3D sensors. In the first framework, we address the problem of pose/motion retrieval in full 3D reconstructed sequences. The human shape representation is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface. It allows efficient shape to shape comparison taking benefits from Riemannian geometry in the open curve shape space. As each human pose represented by this descriptor is viewed as a point in the shape space, we propose to model the motion sequence by a trajectory on this space. Dynamic Time Warping in the feature vector space is then used to compare different motions. In the second framework, we propose a solution for action and gesture recognition from both skeleton and depth data acquired by low cost cameras such as Microsoft Kinect. The action sequence is represented by a dynamical system whose observability matrix is characterized as an element of a Grassmann manifold. Thus, recognition problem is reformulated as a point classification on this manifold. Here, a new learning algorithm based on the notion of tangent spaces is proposed to improve recognition task. Performances of our approach on several benchmarks show high recognition accuracy with low latency

Grassmannian Representation of Motion Depth for 3D Human Gesture and Action Recognition

International audienceRecently developed commodity depth sensors open up new possibilities of dealing with rich descriptors, which capture geometrical features of the observed scene. Here, we propose an original approach to represent geometrical features extracted from depth motion space, which capture both geometric appearance and dynamic of human body simultaneously. In this approach, sequence features are modeled temporally as subspaces lying on Grassmannian manifold. Classification task is carried out via computation of probability density functions on tangent space of each class tacking benefit from the geometric structure of the Grassmaniann manifold. The experimental evaluation is performed on three existing datasets containing various chal- lenges, including MSR-action 3D, UT-kinect and MSR-Gesture3D. Results reveal that our approach outperforms the state-of-the- art methods, with accuracy of 98.21% on MSR-Gesture3D and 95.25% on UT-kinect, and achieves a competitive performance of 86.21% on MSR-action 3D