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Pierre-and-Marie-Curie University, France

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39 Papers

TEATIME: A Formal Model of Action Tendencies in Conversational Agents

Topics: Model-Based Reasoning; Agent Models and Architectures; Conversational Agents

Authors: Alya Yacoubi ¹ and Nicolas Sabouret ²

Affiliations: ¹ LIMSI, CNRS and DAVI les humaniseurs, France , ² LIMSI and CNRS, France

Digital Object Identifier: 10.5220/0006595701430153

Proceedings of ICAART 2018, ISBN: 978-989-758-275-2

Quick Abstract

This paper presents a formal model of socio-affective behaviour in a conversational agent based on the Action Tendency theory. This theory defines emotions as tendencies to perform an action. This theory allows us to implement a strong connection between emotions and speech acts during an agent-human interaction. Our model presents an agent architecture with beliefs, desires, ideals and capacities. It relies on 6 appraisal variables for the selection of different emotional strategies depending on the context of the dialogue. It also supports social regulation of emotions depending on social rules. We implemented this model in an agent architecture and we give an example of dialogue with a virtual insurance expert in the context of customer relationship management.

Related Ontology Subjects/Areas/Topics (6)

Agent Models and Architectures		Artificial Intelligence		Model-Based Reasoning
Agents		Conversational Agents		Symbolic Systems

Ensemble Kalman Filter based on the Image Structures

Topic: Optical Flow and Motion Analyses

Authors: Dominique Béréziat ¹ , Isabelle Herlin ² and Yann Lepoittevin ²

Affiliations: ¹ Sorbonne Universités and UPMC Univ. Paris 06, France , ² Inria, France

Digital Object Identifier: 10.5220/0006096201400150

Proceedings of VISAPP 2017/VISIGRAPP, ISBN: 978-989-758-227-1

Quick Abstract

One major limitation of the motion estimation methods that are available in the literature concerns the availability of the uncertainty on the result. This is however assessed by a number of filtering methods, such as the ensemble Kalman filter (EnKF). The paper consequently discusses the use of a description of the displayed structures in an ensemble Kalman filter, which is applied for estimating motion on image acquisitions. An example of such structure is a cloud on meteorological satellite acquisitions. Compared to the Kalman filter, EnKF does not require propagating in time the error covariance matrix associated to the estimation, resulting in reduced computational requirements. However, EnKF is also known for exhibiting a shrinking effect when taking into account the observations on the studied system at the analysis step. Methods are available in the literature for correcting this shrinking effect, but they do not involve the spatial content of images and more specifically the structures that are displayed on the images. Two solutions are described and compared in the paper, which are first a dedicated localization function and second an adaptive domain decomposition. Both methods proved being well suited for fluid flows images, but only the domain decomposition is suitable for an operational setting. In the paper, the two methods are applied on synthetic data and on satellite images of the atmosphere, and the results are displayed and evaluated.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Motion, Tracking and Stereo Vision

Optical Flow and Motion Analyses

Using Time Use Surveys in Multi Agent based Simulations of Human Activity

Topics: Multi-Agent Systems; Industrial Applications of AI; Simulation

Authors: Quentin Reynaud ¹ , Yvon Haradji ² , François Sempé ³ and Nicolas Sabouret ¹

Affiliations: ¹ LIMSI, CNRS, Univ. Paris-Sud and Université Paris-Saclay, France , ² EDF RandD and EDF Lab Paris-Saclay, France , ³ François Sempé AE, France

Digital Object Identifier: 10.5220/0006189100670077

Proceedings of ICAART 2017, ISBN: 978-989-758-219-6

Quick Abstract

Human behavior simulations in multi agent systems often lack data to calibrate and qualify the representativeness of the simulated behaviors. In this paper, we will show that massive investigations such as time-use surveys allow us to obtain this type of data. At the present time, time-use surveys are mostly used to validate the realism of human activity at a macroscopic level (population scale). In this paper, we present a new method of human behavior generation that combines the use of time-use surveys to calibrate human activities, with a multi agent system enabling simulated behaviors to gain reactivity, autonomy, coordination and realism at a microscopic level (individual scale).

Related Ontology Subjects/Areas/Topics (18)

Agents	Enterprise Information Systems	Multi-Agent Systems
Artificial Intelligence	Industrial Applications of AI	Operational Research
Artificial Intelligence and Decision Support Systems	Information Systems Analysis and Specification	Simulation
Bioinformatics	Knowledge Engineering and Ontology Development	Soft Computing
Biomedical Engineering	Knowledge-Based Systems	Software Engineering
Distributed and Mobile Software Systems	Methodologies and Technologies	Symbolic Systems

Automatic and Generic Evaluation of Spatial and Temporal Errors in Sport Motions

Topics: Image-Based Modeling and 3D Reconstruction; Features Extraction; Event and Human Activity Recognition

Authors: Marion Morel ¹ , Richard Kulpa ² , Anthony Sorel ² , Catherine Achard ¹ and Séverine Dubuisson ¹

Affiliations: ¹ UPMC Sorbonne Universités, France , ² M2S Lab - University Rennes 2, France

Digital Object Identifier: 10.5220/0005778505420551

Proceedings of VISAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

Quick Abstract

Automatically evaluating and quantifying the performance of a player is a complex task since the important motion features to analyze depend on the type of performed action. But above all, this complexity is due to the variability of morphologies and styles of both the experts who perform the reference motions and the novices. Only based on a database of experts' motions and no additional knowledge, we propose an innovative 2-level DTW (Dynamic Time Warping) approach to temporally and spatially align the motions and extract the imperfections of the novice's performance for each joints. In this study, we applied our method on tennis serve but since it is automatic and morphology-independent, it can be applied to any individual motor performance.

Related Ontology Subjects/Areas/Topics (8)

Applications	Geometry and Modeling	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Image and Video Analysis	Software Engineering
Features Extraction	Image-Based Modeling

Information Fusion for Action Recognition with Deeply Optimised Hough Transform Paradigm

Topics: Segmentation and Grouping; Event and Human Activity Recognition

Authors: Geoffrey Vaquette ¹ , Catherine Achard ² and Laurent Lucat ¹

Affiliations: ¹ CEA and LIST, France , ² Sorbonne University, UPMC Univ Paris 06 and CNRS, France

Digital Object Identifier: 10.5220/0005725604230430

Proceedings of VISAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

Quick Abstract

Automatic human action recognition is a challenging and largely explored domain. In this work, we focus on action segmentation with Hough Transform paradigm and more precisely with Deeply Optimised Hough Transform (DOHT). First, we apply DOHT on video sequences using the well-known dense trajectories features and then, we propose to extend the method to efficiently merge information coming from various sensors. We have introduced three different ways to perform fusion, depending on the level at which information is merged. Advantages and disadvantages of these solutions are presented from the performance point of view and also according to the ease of use. Thus, one of the fusion level has the advantage to stay suitabe even if one or more sensors is out of order or disturbed.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Segmentation and Grouping

Towards the Rectification of Highly Distorted Texts

Topics: Shape Representation and Matching; Features Extraction; Document Imaging in Business

Authors: Stefania Calarasanu ¹ , Séverine Dubuisson ² and Jonathan Fabrizio ¹

Affiliations: ¹ EPITA-LRDE, France , ² Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 7222 and ISIR, France

Digital Object Identifier: 10.5220/0005772602410248

Proceedings of VISAPP 2016/VISIGRAPP, ISBN: 978-989-758-175-5

Quick Abstract

A frequent challenge for many Text Understanding Systems is to tackle the variety of text characteristics in born-digital and natural scene images to which current OCRs are not well adapted. For example, texts in perspective are frequently present in real-word images, but despite the ability of some detectors to accurately localize such text objects, the recognition stage fails most of the time. Indeed, most OCRs are not designed to handle text strings in perspective but rather expect horizontal texts in a parallel-frontal plane to provide a correct transcription. In this paper, we propose a rectification procedure that can correct highly distorted texts, subject to rotation, shearing and perspective deformations. The method is based on an accurate estimation of the quadrangle bounding the deformed text in order to compute a homography to transform this quadrangle (and its content) into a horizontal rectangle. The rectification is validated on the dataset proposed during the ICDAR 2015 Competition on Scene Text Rectification.

Related Ontology Subjects/Areas/Topics (6)

Applications and Services		Document Imaging in Business		Image and Video Analysis
Computer Vision, Visualization and Computer Graphics		Features Extraction		Shape Representation and Matching

A Self-adaptive Likelihood Function for Tracking with Particle Filter

Topic: Tracking and Visual Navigation

Authors: Séverine Dubuisson ¹ , Myriam Robert-Seidowsky ² and Jonathan Fabrizio ²

Affiliations: ¹ CNRS, UMR 7222 and ISIR, France , ² LRDE-EPITA, France

Digital Object Identifier: 10.5220/0005260004460453

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-091-8

Quick Abstract

The particle filter is known to be efficient for visual tracking. However, its parameters are empirically fixed, depending on the target application, the video sequences and the context. In this paper, we introduce a new algorithm which automatically adjusts online two majors of them: the correction and the propagation parameters. Our purpose is to determine, for each frame of a video, the optimal value of the correction parameter and to adjust the propagation one to improve the tracking performance. On one hand, our experimental results show that the common settings of particle filter are sub-optimal. On another hand, we prove that our approach achieves a lower tracking error without needing to tune these parameters. Our adaptive method allows to track objects in complex conditions (illumination changes, cluttered background, etc.) without adding any computational cost compared to the common usage with fixed parameters.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Motion, Tracking and Stereo Vision

Tracking and Visual Navigation

An Image-based Ensemble Kalman Filter for Motion Estimation

Topic: Optical Flow and Motion Analyses

Authors: Yann Lepoittevin ¹ , Isabelle Herlin ¹ and Dominique Béréziat ²

Affiliations: ¹ Inria Paris-Rocquencourt, France , ² UPMC Univ Paris 06, CNRS and UMR 7606, France

Digital Object Identifier: 10.5220/0005259804370445

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-091-8

Quick Abstract

This paper designs an Image-based Ensemble Kalman Filter (IEnKF), whose components are defined only from image properties, to estimate motion on image sequences. The key elements of this filter are, first, the construction of the initial ensemble, and second, the propagation in time of this ensemble on the studied temporal interval. Both are analyzed in the paper and their impact on results is discussed with synthetic and real data experiments. The initial ensemble is obtained by adding a Gaussian vector field to an estimate of motion on the first two frames. The standard deviation of this normal law is computed from motion results given by a set of optical flow methods of the literature. It describes the uncertainty on the motion value at initial date. The propagation in time of the ensemble members relies on the following evolution laws: transport by velocity of the image brightness function and Euler equations for the motion function. Shrinking of the ensemble is avoided thanks to a localization method and the use of observation ensembles, both techniques being defined from image characteristics. This Image-based Ensemble Kalman Filter is quantified on synthetic experiments and applied on traffic and meteorological images.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Motion, Tracking and Stereo Vision

Optical Flow and Motion Analyses

Launch These Manhunts! Shaping the Synergy Maps for Multi-camera Detection

Topics: Camera Networks and Vision; Shape Representation and Matching; Object Detection and Localization; Video Surveillance and Event Detection

Authors: Muhammad Owais Mehmood ¹ , Sébastien Ambellouis ¹ and Catherine Achard ²

Affiliations: ¹ French Institute of Science and Technology for Transport, Spatial Planning and Development and Networks, France , ² Sorbonne Universites, UPMC Univ Paris 06 and CNRS UMR 7222 and ISIR, France

Digital Object Identifier: 10.5220/0005355805280535

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-090-1

Quick Abstract

We present a method for multi-camera people detection based on the multi-view geometry. We propose to create a synergy map by the projection of foreground masks across all camera views on the ground plane and the planes parallel to the ground. This leads to significant values on locations where people are present, and also to a particular shape around these values. Moreover, a well-known ghost phenomena appears i.e. when these shapes corresponding to different persons are fused then the false detections are also generated. In this article, the first improvement is the robust detection of the candidate detection locations, namely keypoints, from the synergy map based on a watershed transform. Then, in order to reduce the false positives, mainly due to the ghost phenomena, we check if the particular shape, for an ideal person, is present or not. This shape, that is different for each location of the synergy map, is generated for each keypoint, assuming the presence of a person, and with the knowledge of the scene geometry. Finally, the real shape and the synthetic one are compared using a similarity measure that is similar to correlation. Another improvement proposed in this article is the use of unsupervised clustering, performed on the measures obtained at all the keypoints. It allows to automatically find the optimal threshold on the measure, and thus to decide about people detection. We have compared our method to the recent state-of-the-art techniques on a publicly available dataset and have shown that it reduces the detection errors.

Related Ontology Subjects/Areas/Topics (7)

Applications and Services	Image and Video Analysis	Shape Representation and Matching
Camera Networks and Vision	Motion, Tracking and Stereo Vision	Video Surveillance and Event Detection
Computer Vision, Visualization and Computer Graphics

What’s next? Different Strategies Considering Teachers’ Decisions for Adapting Learning Paths in Serious Games

Topics: Intelligent Learning and Teaching Systems; Game-Based and Simulation-Based Learning; Vocational Training Tools and Methods

Authors: Javier Melero , Naïma El-Kechaï and Jean-Marc Labat

Affiliation: Université Pierre et Marie Curie, France

Digital Object Identifier: 10.5220/0005448201010108

Proceedings of CSEDU 2015, ISBN: 978-989-758-108-3

Quick Abstract

Adapting Serious Games (SGs) plays an important role to offer personalized game experiences. A well-fitting approach to create adaptive SGs is based on Competence-based Knowledge Space Theory (CbKST). CbKST structures the SG activities with respect to knowledge and competences, and adaptation is based on suggesting activities that improve learners’ competences. However, differences among learners and the diversity of learning situations may drive teachers to use different adaptive approaches to address their own needs. In addition to the current state of learners’ competences, we also propose to consider teachers’ decisions as a key parameter for adapting learning paths in SGs. As part of Play Serious project, several teachers’ requirements have been identified. This paper presents three different recommendation strategies based on the identified requirements, to build adaptive learning paths in SGs.

Related Ontology Subjects/Areas/Topics (8)

Computer-Supported Education	Game-Based and Simulation-Based Learning	Social Context and Learning Environments
Domain Applications and Case Studies	Intelligent Learning and Teaching Systems	Vocational Training Tools and Methods
e-Learning	Learning/Teaching Methodologies and Assessment

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