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INRIA, France

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13 Papers

STTL - A SPARQL-based Transformation Language for RDF

Topics: XML and Data Management; Metadata and Metamodeling; Ontology and the Semantic Web

Authors: Olivier Corby ¹ and Catherine Faron-Zucker ²

Affiliations: ¹ Inria Sophia Antipolis Méditerranée, France , ² Univ. Nice Sophia Antipolis, France

Digital Object Identifier: 10.5220/0005450604660476

Proceedings of WEBIST 2015, ISBN: 978-989-758-106-9

Quick Abstract

The general research question addressed in this paper is How to transform RDF into other languages. This is of prime interest to present data selected and extracted from the Web of data in a format suitable for the user (e.g., HTML or CSV). Moreover, RDF can be viewed as a meta-model to represent on the Web of data other languages and models. The above research question then becomes How to generate the concrete syntax of expressions of a given language from their RDF representation. To answer these questions, we present SPARQL Template Transformation Language (STTL), a generic RDF transformation rule language, independent from the output language. We conceived it as a lightweight syntactic extension to SPARQL and we show how to compile it into standard SPARQL. We present a generic transformation rule engine implementing STTL and several RDF transformers we defined for various output languages, showing STTL’s expressive power.

Related Ontology Subjects/Areas/Topics (6)

Internet Technology		Ontology and the Semantic Web		Web Interfaces and Applications
Metadata and Metamodeling		Web Information Systems and Technologies		XML and Data Management

Feature Matching using CO-Inertia Analysis for People Tracking

Topic: Tracking and Visual Navigation

Authors: Srinidhi Mukanahallipatna Simha , Duc Phu Chau and Francois Bremond

Affiliation: INRIA, France

Digital Object Identifier: 10.5220/0004669502800287

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-009-3

Quick Abstract

Robust object tracking is a challenging computer vision problem due to dynamic changes in object pose, illumination, appearance and occlusions. Tracking objects between frames requires accurate matching of their features. We investigate real time matching of mobile object features for frame to frame tracking. This paper presents a new feature matching approach between objects for tracking that incorporates one of the multivariate analysis method called Co-Inertia Analysis abbreviated as COIA. This approach is being introduced to compute the similarity between Histogram of Oriented Gradients (HOG) features of the tracked objects. Experiments conducted shows the effectiveness of this approach for mobile object feature tracking.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Motion, Tracking and Stereo Vision

Tracking and Visual Navigation

Group Tracking and Behavior Recognition in Long Video Surveillance Sequences

Topics: Event and Human Activity Recognition; Video Surveillance and Event Detection

Authors: Carolina Gárate , Sofia Zaidenberg , Julien Badie and François Brémond

Affiliation: INRIA, France

Digital Object Identifier: 10.5220/0004682503960402

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-004-8

Quick Abstract

This paper makes use of recent advances in group tracking and behavior recognition to process large amounts of video surveillance data from an underground railway station and perform a statistical analysis. The most important advantages of our approach are the robustness to process long videos and the capacity to recognize several and different events at once. This analysis automatically brings forward data about the usage of the station and the various behaviors of groups in different hours of the day. This data would be very hard to obtain without an automatic group tracking and behavior recognition method. We present the results and interpretation of one month of processed data from a video surveillance camera in the Torino subway.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Motion, Tracking and Stereo Vision

Video Surveillance and Event Detection

Retrieval System for Person Re-identification

Authors: Sławomir Bąk ¹ , François Brémond ¹ , Vasanth Bathrinarayanan ¹ , Alessandro Capra ² , Davide Giacalone ² , Giuseppe Messina ² and Antonio Buemi ²

Affiliations: ¹ INRIA and STARS group, France , ² STMicroelectronics, Italy

Digital Object Identifier: 10.5220/0004871807320738

Proceedings of PANORAMA 2014/VISIGRAPP, ISBN: 978-989-758-004-8

Quick Abstract

This paper addresses the problem of person re-identification and its application to a real world scenario. We introduce a retrieval system that helps a human operator in browsing a video content. This system is designed for determining whether a given person of interest has already appeared over a network of cameras. In contrast to most of state of the art approaches we do not focus on searching the best strategy for feature matching between camera pairs, but we explore techniques that can perform relatively well in a whole network of cameras. This work is devoted to analyze current state of the art algorithms and technologies, which are currently available on the market. We examine whether current techniques may help a human operator in solving this challenging task. We evaluate our system on the publicly available dataset and demonstrate practical advantages of the proposed solutions.

Towards Reliable Real-time Person Detection

Topics: Machine Learning Technologies for Vision; Features Extraction; Object Detection and Localization

Authors: Silviu-Tudor serban , Srinidhi Mukanahallipatna Simha , Vasanth Bathrinarayanan , Etienne Corvee and Francois Bremond

Affiliation: INRIA Sophia Antipolis - Mediterranee, France

Digital Object Identifier: 10.5220/0004651302320239

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-004-8

Quick Abstract

We propose a robust real-time person detection system, which aims to serve as solid foundation for developing solutions at an elevated level of reliability. Our belief is that clever handling of input data correlated with efficacious training algorithms are key for obtaining top performance. We introduce a comprehensive training method based on random sampling that compiles optimal classifiers with minimal bias and overfit rate. Building upon recent advances in multi-scale feature computations, our approach attains state-of-the-art accuracy while running at high frame rate.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Features Extraction

Image and Video Analysis

Towards Unsupervised Sudden Group Movement Discovery for Video Surveillance

Topics: Camera Networks and Vision; Categorization and Scene Understanding; Event and Human Activity Recognition; Video Surveillance and Event Detection

Authors: Sofia Zaidenberg , Piotr Bilinski and François Brémond

Affiliation: Inria, France

Digital Object Identifier: 10.5220/0004682403880395

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-004-8

Quick Abstract

This paper presents a novel and unsupervised approach for discovering “sudden” movements in video surveillance videos. The proposed approach automatically detects quick motions in a video, corresponding to any action. A set of possible actions is not required and the proposed method successfully detects potentially alarm-raising actions without training or camera calibration. Moreover, the system uses a group detection and event recognition framework to relate detected sudden movements and groups of people, and provide a semantical interpretation of the scene. We have tested our approach on a dataset of nearly 8 hours of videos recorded from two cameras in the Parisian subway for a European Project. For evaluation, we annotated 1 hour of sequences containing 50 sudden movements.

Related Ontology Subjects/Areas/Topics (5)

Applications and Services		Computer Vision, Visualization and Computer Graphics		Video Surveillance and Event Detection
Camera Networks and Vision		Motion, Tracking and Stereo Vision

Optimized Cascade of Classifiers for People Detection using Covariance Features

Topic: Object Detection and Localization

Authors: Malik Souded ¹ and Francois Bremond ²

Affiliations: ¹ INRIA Sophia Antipolis - Méditerranée and Digital Barriers France, France , ² INRIA Sophia Antipolis - Méditerranée, France

Digital Object Identifier: 10.5220/0004304208200826

Proceedings of VISAPP 2013/VISIGRAPP, ISBN: 978-989-8565-47-1

Quick Abstract

People detection on static images and video sequences is a critical task in many computer vision applications, like image retrieval and video surveillance. It is also one of most challenging task due to the large number of possible situations, including variations in people appearance and poses. The proposed approach optimizes an existing approach based on classification on Riemannian manifolds using covariance matrices in a boosting scheme, making training and detection faster while maintaining equivalent performances. This optimisation is achieved by clustering negative samples before training, providing a smaller number of cascade levels and less weak classifiers in most levels in comparison with the original approach. Our work was evaluated and validated on INRIA Person dataset.

PEOPLE DETECTION AND RE-IDENTIFICATION FOR MULTI SURVEILLANCE CAMERAS

Topics: Image and Video Coding and Compression; Camera Networks and Vision; Features Extraction; Object and Face Recognition; Video Surveillance and Event Detection

Authors: Etienne Corvee , Slawomir Bak and François Brémond

Affiliation: INRIA, France

Digital Object Identifier: 10.5220/0003808600820088

Proceedings of VISAPP 2012/VISIGRAPP, ISBN: 978-989-8565-03-7

Quick Abstract

Re-identifying people in a network of non overlapping cameras requires people to be accurately detected and tracked in order to build a strong visual signature of people appearances. Traditional surveillance cameras do not provide high enough image resolution to iris recognition algorithms. State of the art face recognition can not be easily applied to surveillance videos as people need to be facing the camera at a close range. The different lighting environment contained in each camera scene and the strong illumination variability occurring as people walk throughout a scene induce great variability in their appearance. In addition, people images occlud each other onto the image plane making people detection difficult to achieve. We propose a novel simplified Local Binary Pattern features to detect people, head and faces. A Mean Riemannian Covariance Grid (MRCG) is used to model appearance of tracked people to obtain highly discriminative human signature. The methods are evaluated and compared with the state of the art algorithms. We have created a new dataset from a network of 2 cameras showing the usefulness of our system to detect, track and re-identify people using appearance and face features.

Related Ontology Subjects/Areas/Topics (9)

Applications and Services	Features Extraction	Image Formation and Preprocessing
Camera Networks and Vision	Image and Video Analysis	Motion, Tracking and Stereo Vision
Computer Vision, Visualization and Computer Graphics	Image and Video Coding and Compression	Video Surveillance and Event Detection

ROBUST MOBILE OBJECT TRACKING BASED ON MULTIPLE FEATURE SIMILARITY AND TRAJECTORY FILTERING

Topics: Motion and Tracking; Matching Correspondence and Flow; Tracking of People and Surveillance

Authors: Duc Phu Chau , François Bremond , Monique Thonnat and Etienne Corvee

Affiliation: INRIA Sophia Antipolis - Méditerranée, France

Digital Object Identifier: 10.5220/0003317205690574

Proceedings of VISAPP 2011/VISIGRAPP, ISBN: 978-989-8425-47-8

Quick Abstract

This paper presents a new algorithm to track mobile objects in different scene conditions. The main idea of the proposed tracker includes estimation, multi-features similarity measures and trajectory filtering. A feature set (distance, area, shape ratio, color histogram) is defined for each tracked object to search for the best matching object. Its best matching object and its state estimated by the Kalman filter are combined to update position and size of the tracked object. However, the mobile object trajectories are usually fragmented because of occlusions and misdetections. Therefore, we also propose a trajectory filtering, named global tracker, aims at removing the noisy trajectories and fusing the fragmented trajectories belonging to a same mobile object. The method has been tested with five videos of different scene conditions. Three of them are provided by the ETISEO benchmarking project (http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker performance has been compared with other seven tracking algorithms. The advantages of our approach over the existing state of the art ones are: (i) no prior knowledge information is required (e.g. no calibration and no contextual models are needed), (ii) the tracker is more reliable by combining multiple feature similarities, (iii) the tracker can perform in different scene conditions: single/several mobile objects, weak/strong illumination, indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to improve the tracker performance, (v) the tracker performance outperforms many algorithms of the state of the art. filtering is defined and applied to improve the tracker performance, (v) the tracker performance outperforms many algorithms of the state of the art.

Related Ontology Subjects/Areas/Topics (10)

Applications	Methodologies and Methods	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Motion and Tracking	Physiological Computing Systems
Human-Computer Interaction	Motion, Tracking and Stereo Vision	Tracking of People and Surveillance
Matching Correspondence and Flow

BEARING-ONLY SAM USING A MINIMAL INVERSE DEPTH PARAMETRIZATION - Application to Omnidirectional SLAM

Topics: Vision, Recognition and Reconstruction; Mobile Robots and Autonomous Systems

Authors: Cyril Joly and Patrick Rives

Affiliation: INRIA Sophia Antipolis Méditerranée, France

Digital Object Identifier: 10.5220/0002949702810288

Proceedings of ICINCO 2010, ISBN: 978-989-8425-01-0

Quick Abstract

Safe and efficient navigation in large-scale unknown environments remains a key problem which has to be solved to improve the autonomy of mobile robots. SLAM methods can bring the map of the world and the trajectory of the robot. Monucular visual SLAMis a difficult problem. Currently, it is solved with an Extended Kalman Filter (EKF) using the inverse depth parametrization. However, it is now well known that the EKFSLAMbecome inconsistent when dealing with large scale environments. Moreover, the classical inverse depth parametrization is over-parametrized, which can also be a cause of inconsistency. In this paper, we propose to adapt the inverse depth representation to the more robust context of smoothing and mapping (SAM).We show that our algorithm is not over-parameterized and that it gives very accurate results on real data.

Related Ontology Subjects/Areas/Topics (4)

Informatics in Control, Automation and Robotics		Robotics and Automation		Vision, Recognition and Reconstruction
Mobile Robots and Autonomous Systems

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