The Attitudes of the workers towards the moral motivation systems in the economic institution “Sufia Souk Ahras”

هـــدفت الدراسة إلى تحــديد إتجــاهات العاملين نحو نظـــام الحوافــز المعنـــوية في المـــؤســـسة الإقتــــصادية "صوفية سوق أهراس".من أجل ذلك تم إختيار عينة تتكون من 97 عامل مقسمة حسب الجنس إلى (77 عامل ذكر) و(20عامل أنثى) في الفترة الزمنية الممتدة ما بين 2016-2017.توصلت نتائج الدراسة إلى أن اتجاهات العاملين نحو أنظمة الحوافز المعنوية بالموافقة حيث لم يتم تسجيل فروقات ذات دلالة إحصائية لكلا الجنسينA sample of 97 sex workers was assigned to 77 male workers and 20 female workers in the period between 2016 and 2017 results of the study showed that the attitudes of workers towards the systems of moral motivation in the approval where no significant differences were recorded for both sexe

Human Body Part Labeling and Tracking Using Graph Matching Theory

International audienceProperly labeling human body parts in video sequencesis essential for robust tracking and motion interpretationframeworks. We propose to perform this task by usingGraph Matching. The silhouette skeleton is computed anddecomposed into a set of segments corresponding to the differentlimbs. A Graph capturing the topology of the segmentsis generated and matched against a 3D model of thehuman skeleton. The limb identification is carried out foreach node of the graph, potentially leading to the absenceof correspondence. The method captures the minimal informationabout the skeleton shape. No assumption about theviewpoint, the human pose, the geometry or the appearenceof the limbs is done during the matching process, making theapproach applicable to every configuration. Some correspondancesthat might be ambiguous only relying on topologyare enforced by tracking each graph node over time.Several results present the efficiency of the labeling, particularlyits robustness to limb detection errors that are likelyto occur in real situations because of occlusions or low levelsystem failures. Finally the relevance of the labeling in anoverall tracking system is pointed out

EYE AND GAZE TRACKING ALGORITHM FOR COLLABORATIVE LEARNING SYSTEM

International audienceOur work focuses on the interdisciplinary field of detailed analysis of behaviors exhibited by individuals during sessions of distributed collaboration. With a particular focus on ergonomics, we propose new mechanisms to be integrated into existing tools to enable increased productivity in distributed learning and working. Our technique is to record ocular movements (eye tracking) to analyze various scenarios of distributed collaboration in the context of computer-based training. In this article, we present a low-cost oculometric device that is capable of making ocular measurements without interfering with the natural behavior of the subject. We expect that this device could be employed anywhere that a natural, non-intrusive method of observation is required, and its low-cost permits it to be readily integrated into existing popular tools, particularly E-learning campus

Harvesting effects, recovery mechanisms, and management strategies for a long-lived and structural precious coral

Overexploitation is a major threat for the integrity of marine ecosystems. Understanding the ecological consequences of different extractive practices and the mechanisms underlying the recovery of populations is essential to ensure sustainable management plans. Precious corals are long-lived structural invertebrates, historically overfished, and their conservation is currently a worldwide concern. However, the processes underlying their recovery are poorly known. Here, we examined harvesting effects and recovery mechanisms of red coral Corallium rubrum by analyzing long-term photographic series taken on two populations that were harvested. We compared the relative importance of reproduction and re-growth as drivers of resilience. Harvesting heavily impacted coral populations causing large de- creases in biomass and strong size-class distribution shifts towards populations dominated by small colonies. At the end of the study (after 4 and 7 years) only partial recovery was ob- served. The observed general pattern of low recruitment and high mortality of new recruits demonstrated limited effects of reproduction on population recovery. Adversely, low mortali- ty of partially harvested adults and a large proportion of colonies showing new branches highlighted the importance of re-growth in the recovery process. The demographic projec- tions obtained through stochastic models confirmed that the recovery rates of C. rubrum can be strongly modulated depending on harvesting procedures. Thus, leaving the basal section of the colonies when harvesting to avoid total mortality largely enhances the resil- ience of C. rubrum populations and quickens their recovery. On the other hand, the high survival of harvested colonies and the significant biomass reduction indicated that abun- dance may not be an adequate metric to assess the conservation status of clonal organisms because it can underestimate harvesting effects. This study highlights the unsustainability of current harvesting practices of C. rubrum and provides urgently needed data to improve management practices that are still largely based on untested assumptions

Reconnaissance 2D/2D et 2D/3D d'objets à partir de leurs squelettes

EVRY-BU (912282101) / SudocSudocFranceF

Toward 3D free form object tracking using skeleton

International audienceIn this paper we describe an original method for the 3D free form object tracking in monocular vision. The main contribution of this article is the use of the skeleton of an object in order to recognize, locate and track this object in real time. Indeed, the use of this kind of representation made it possible to avoid difficulties related to the absence of prominent elements in free form objects (which makes the matching process easier). The skeleton is a lower dimension representation of the object, it is homotopic and it has a graph structure. This allowed us to use powerful tools of the graph theory in order to perform matching between scene objects and models (recognition step). Thereafter, we used skeleton extremities as interest points for the tracking

Human Body Part Labeling and Tracking Using Graph Matching Theory

International audienceProperly labeling human body parts in video sequencesis essential for robust tracking and motion interpretationframeworks. We propose to perform this task by usingGraph Matching. The silhouette skeleton is computed anddecomposed into a set of segments corresponding to the differentlimbs. A Graph capturing the topology of the segmentsis generated and matched against a 3D model of thehuman skeleton. The limb identification is carried out foreach node of the graph, potentially leading to the absenceof correspondence. The method captures the minimal informationabout the skeleton shape. No assumption about theviewpoint, the human pose, the geometry or the appearenceof the limbs is done during the matching process, making theapproach applicable to every configuration. Some correspondancesthat might be ambiguous only relying on topologyare enforced by tracking each graph node over time.Several results present the efficiency of the labeling, particularlyits robustness to limb detection errors that are likelyto occur in real situations because of occlusions or low levelsystem failures. Finally the relevance of the labeling in anoverall tracking system is pointed out

Learning Articulated Appearance Models for Tracking Humans: a Spectral Graph Matching Approach

International audienceTracking an unspecified number of people in real-time is one of the most challenging tasks in computer vision. In this paper, we propose an original method to achieve this goal, based on the construction of a 2D human appearance model. The general framework, which is a region-based tracking approach, is applicable to any type of object. We show how to specialize the method for taking advantage of the structural properties of the human body. We segment its visible parts by using a skeletal graph matching strategy inspired by the shock graphs. Only morphological and topological information is encoded in the model graph, making the approach independent of the pose of the person, the viewpoint, the geometry or the appearance of the limbs. The limbs labeling makes it possible to build and update an appearance model for each body part. The resulting discriminative feature, that we denote as an articulated appearance model, captures both color, texture and shape properties of the different limbs. It is used to identify people in complex situations (occlusion, field of view exit, etc.), and maintain the tracking. The model to image matching has proved to be much more robust and better-founded than with existing global appearance descriptors, specifically when dealing with highly deformable objects such as humans. The only assumption for the recognition is the approximate viewpoint correspondence between the different models during the matching process. The method does not make use of skin color detection, which allows us to perform tracking under any viewpoint. Occlusions can be detected by the generic part of the algorithm, and the tracking is performed in such cases by means of a particle filter. Several results in complex situations prove the capacity of the algorithm to learn people appearance in unspecified poses and viewpoints, and its efficiency for tracking multiple humans in real-time using the specific updated descriptors. Finally, the model provides an important clue for further human motion analysis process

Human Body Part Labeling and Tracking Using Graph Matching Theory

International audienceProperly labeling human body parts in video sequencesis essential for robust tracking and motion interpretationframeworks. We propose to perform this task by usingGraph Matching. The silhouette skeleton is computed anddecomposed into a set of segments corresponding to the differentlimbs. A Graph capturing the topology of the segmentsis generated and matched against a 3D model of thehuman skeleton. The limb identification is carried out foreach node of the graph, potentially leading to the absenceof correspondence. The method captures the minimal informationabout the skeleton shape. No assumption about theviewpoint, the human pose, the geometry or the appearenceof the limbs is done during the matching process, making theapproach applicable to every configuration. Some correspondancesthat might be ambiguous only relying on topologyare enforced by tracking each graph node over time.Several results present the efficiency of the labeling, particularlyits robustness to limb detection errors that are likelyto occur in real situations because of occlusions or low levelsystem failures. Finally the relevance of the labeling in anoverall tracking system is pointed out

Fast People Counting using Head Detection from Skeleton Graph

International audienceIn this paper, we present a new method for counting people. This method is based on the head detection after a segmentation of the human body by skeleton graph process. The skeleton silhouette is computed and decomposed into a set of segments corresponding to the head, torso and limbs. This structure captures the minimal information about the skeleton shape. No assumption is made about the viewpoint, this is done after the head pose process. Several results present the efficiency of the labelling process , particularly its structural properties for the detection of heads within a crowd. A proposed method has been tested with an experiment of counting the number of pedestrians passing in a specific area