Laboratoire de Génie Informatique et d’Automatique de l’Artois

Samir HACHOUR

Associate professor
Member of the research themes:
Contact information:

Revue Internationale avec Comité de Lecture

Samir HACHOUR -- François DELMOTTE -- David MERCIER
A Robust Credal Assignment Solution Based on the Generalized Bayes’ Theorem
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, pp 947–971, Vol. 25, No. 6, 12/2017
Benoit FORTIN -- Samir HACHOUR -- François DELMOTTE
Multi-Target PHD Tracking and Classification Using Imprecise Likelihoods
In International Journal of Approximate Reasoning, pp 17-36, Vol. 90, 11/2017
Object tracking and credal classification with kinematic data in a multi-target context
Information Fusion, pp 174-188, Vol. 20, , 11/2014

Conférence Internationale avec Comité de Lecture

2022
International conference with review committee
Inventory Control in Supply Chain: A Model-Free Approach
10th IFAC Conference on Manufacturing, Modelling, Management and Control, MIM’22, Nantes, France, June, 22-24, 06/2022
2021
International conference with review committee
DOI
Improving an Evidential Source of Information Using Contextual Corrections Depending on Partial Decisions
International Conference on Belief Functions 2021, pp 247-256, Shanghai, China, 10/2021
A Belief Function Solution for Stator Insulation Robustness Study
9th International Conference on Power and Energy Systems (ICPES), 12/2019
On Learning Evidential Contextual Corrections from Soft Labels Using a Measure of Discrepancy Between Contour Functions
Proceedings of the 13th international conference on Scalable Uncertainty Management, SUM 2019, pp 382-389, Compiègne, France, 12/2019
Samir HACHOUR -- François DELMOTTE -- David MERCIER
A new parameterless credal method to track-to-track assignment problem
3rd International Conference on Belief Functions, BELIEF 2014, pp 403-411, Oxford, United Kingdom, F. Cuzzolin (Ed.), 09/2014
Samir HACHOUR -- François DELMOTTE -- David MERCIER
A distributed solution for multi-object tracking and classification
17th International Conference on Information Fusion, FUSION 2014, Salamanca, Spain, , paper 323, 07/2014
2014
International conference with review committee
Samir HACHOUR -- François DELMOTTE -- David MERCIER
Comparison of credal assignment algorithms in kinematic data tracking context
15th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference, IPMU 2014, pp 200-211, Montpellier, France, 07/2014
Multi-Sensor multi-target tracking with robust kinematic data based credal classification
8th Workshop, Sensor Data Fusion: Trends, Solutions, Application , SDF 2013, Bonn, Germany, 10/2013

Conférence Nationale avec Comité de Lecture

2021
French conference with review committee
Corrections contextuelles crédibilistes en fonction de décisions partielles
30e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2021, pp 217-224, Paris, France, 10/2021
2013
French conference with review committee
Fusion d’Informations pour la Classification Multi-capteurs, Multi-cibles
22èmes Rencontres Francophones sur la Logique Floue et ses Applications , 10-11 octobre , LFA 2013, pp 111-118, Reims, 01/2013
Tracking and Identification of Multiple Targets
7ème Workshop Interdisciplinaire sur la Sécurité Globale, WISG, No. 13, Troyes, France, 01/2013
Classification crédale multi-cibles
21e Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2012), pp 201-208, Compiègne, France, 11/2012

Livre Scientifique

2015
Scientific book
Samir HACHOUR -- François DELMOTTE -- David MERCIER
Belief function based multisensor multitarget classification solution
Multisensor data fusion, From algorithms and architectural design to applications, pp 331-348, H. Fourati (Ed.), CRC Press, 08/2015

Author of the Ph.D. thesis "Multi-object tracking and classification : contributions with belief functions theory"

2011 - 2015

This thesis deals with multi-objet tracking and classification problem. It was shown that belieffunctions allow the results of classical Bayesian methods to be improved. In particular, a recentapproach dedicated to a single object classification which is extended to multi-object framework. Itwas shown that detected observations to known objects assignment is a fundamental issue in multiobjecttracking and classification solutions. New assignment solutions based on belief functionsare proposed in this thesis, they are shown to be more robust than the other credal solutions fromrecent literature. Finally, the issue of multi-sensor classification that requires a second phase ofassignment is addressed. In the latter case, two different multi-sensor architectures are proposed, aso-called centralized one and another said distributed. Many comparisons illustrate the importanceof this work, in both situations of constant and changing objects classes.

Danielle NYAKAM NYA

2020 -

Siti MUTMAINAH

2017 - 2021

Learning to adjust an evidential source of information using partially labeled data and partial decisions

Keywords:
Belief functions, Correction, Learning, Partial Data

GS2RI (ELSAT 2020)

2015 - 2022

Greener and Safer Rail Road Interaction

Summary :

L’objet du projet PSCHITT_Rail est de réaliser la co-simulation ferroviaire dans un réseau urbain entre des tramways d’une part, et des véhicules et piétons d’autre part, dans le but d’améliorer la sécurité des usagers.