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HAL Id: tel-00621202 https://tel.archives-ouvertes.fr/tel-00621202 Submitted on 9 Sep 2011 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Contributions to the Information Fusion : application to Obstacle Recognition in Visible and Infrared Images Anca Ioana Apatean To cite this version: Anca Ioana Apatean. Contributions to the Information Fusion : application to Obstacle Recognition in Visible and Infrared Images. Other [cs.OH]. INSA de Rouen; Universitatea tehnica (Cluj-Napoca, Roumanie), 2010. English. NNT : 2010ISAM0032. tel-00621202 TECHNICAL UNIVERSITY OF CLUJ-NAPOCA, CLUJ-NAPOCA, ROMÂNIA FACULTY OF ELECTRONICS, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY and INSTITUT NATIONAL DES SCIENCES APPLIQUEES, ROUEN, FRANCE LABORATOIRE D’INFORMATIQUE, DE TRAITEMENT DE L’INFORMATION ET DES SYSTEMES Contributions to the Information Fusion. Application to Obstacle Recognition in Visible and Infrared Images Ph.D. Student: Anca DISCANT (épouse Apˇ atean) Ph.D. Advisor: Professor A. Bensrhair Institut National des Sciences Appliquées, Rouen, France Ph.D. Advisor: Associate Professor A. Rogozan Institut National des Sciences Appliquées, Rouen, France Ph.D. Advisor: Professor C. Rusu Technical University of Cluj-Napoca, Romania 2010 THÈSE DE DOCTORAT Contributions à la fusion des informations. Application à la reconaissance des obstacle dans les images visible et infrarouge présentée et soutenue publiquement le vendredi 15 octobre 2010 pour l’obtention du grade de Docteur de l’Institut National des Sciences Appliquées de Rouen, France et de l’Université Technique de Cluj-Napoca, România par Anca DISCANT (épouse Apˇ atean) Composition du jury : Rapporteurs : Fabrice Meriaudeau - Professeur des Universités, LE2I, IUT Le Creusot, France Vasile Buzuloiu - Professeur des Universités, LAPI, Université Technique de Bucuresti, Roumanie Examinateur : Eugen Lupu - Professeur des Universités, ETTI, Université Technique de Cluj-Napoca, Roumanie Directeurs : Corneliu Rusu - Professeur des Universités, ETTI, Université Technique de Cluj-Napoca, Roumanie Abdelaziz Bensrhair - Professeur des Universités, LITIS, INSA de Rouen, France Encadrante : Alexandrina Rogozan - Maître de Conférences, LITIS, INSA de Rouen, France Dedication Je dédie cette thèse à mon mari qui m’a toujours soutenue, aide et encouragée et que j’aime tant. 3 Acknowledgments This dissertation would have never been ﬁnished without the help of many people, to whom I would like to express my sincere gratitude. I want to thank my advisers professor Corneliu RUSU and professor Abdelaziz BENSRHAIR for their patience and encouragement. They helped me with scientiﬁc and ﬁnancial support during my Ph. D. stage. I want to especially thank to Alexandrina ROGOZAN, an extraordinary person who helped me in a scientiﬁc, organisational and personal way. I wish to thank her also for the time that she dedicated to me during the last years. She always made time to answer my questions, and her advices, observations and supports were and are very valuable for me. I want to thank also to professor Eugen LUPU and my colleague Simina EMERICH from UTCN for their understanding and trust in my ability to complete this work. I would like to address many thanks to all Ph.D. students and staﬀs from INSA who had kindly invited me as a colleague of them, helped me and supported me when I needed. I am very thankful to my families (Discant and Apatean) who sustained and encouraged me during this thesis. I want also to thank to my friend and INSA’s colleague Laura DIOSAN, who was an example for me and inspired me for my research. Anca Apatean (Discant) October 2010 i Abstract The interest for the intelligent vehicle ﬁeld has been increased during the last years, must probably due to an important number of road accidents. Many accidents could be avoided if a device attached to the vehicle would assist the driver with some warnings when dangerous situations are about to appear. In recent years, leading car developers have recorded signiﬁcant eﬀorts and support research works regarding the intelligent vehicle ﬁeld where they propose solutions for the existing problems, especially in the vision domain. Road detection and following, pedestrian or vehicle detection, recognition and tracking, night vision, among others are examples of applications which have been developed and improved recently. Still, a lot of challenges and unsolved problems remain in the intelligent vehicle domain. Our purpose in this thesis is to design an Obstacle Recognition system for improving the road security by directing the driver’s attention towards situations which may become dangerous. Many systems still encounter problems at the detection step and since this task is still a work in progress in the frame of the LITIS laboratory (from INSA), our goal was to develop a system to continue and improve the detection task. We have focused solely on the fusion between the visible and infrared ﬁelds from the viewpoint of an Obstacle Recognition module. Our main purpose was to investigate if the combination of the visible-infrared information is eﬃcient, especially if it is associated with an SVM (Support Vector Machine)-based classiﬁcation. The outdoor environment, the variety of obstacles appearance from the road scene (considering also the multitude of possible types of obstacles), the cluttered background and the fact that the system must cope with the moving vehicle constraints make the categorization of road obstacles a real challenge. In addition, there are some critical requirements that a driver assistance system should fulﬁl in order to be considered a possible solution to be implemented on board of a vehicle: the system cost should be low enough to allow to be incorporated in every series vehicle, the system has to be fast enough to detect and then recognize obstacles in real time, it has to be eﬃcient (to detect all obstacles with very few false alarms) and robust (to be able to face diﬀerent diﬃcult environmental conditions). To outline the system, we were looking for sensors which could provide enough information to detect obstacles (even those occluded) in any illumination or weather situation, to recognize them and to identify their position in the scene. In the intelligent vehicle domain there is no such a perfect sensor to handle all these concerned tasks, but there are systems employing one or many diﬀerent sensors in order to perform obstacles detection, recognition or tracking or some combination of them. After comparing advantages and disadvantages between passive and active technologies, we chose the proper sensors for developing our Obstacle Detection and Recognition system. Due to possible interferences among active sensors, which could be critical for a large number of vehicles moving simultaneously in the same environment, we concentrate on using passive sensors, which are non-invasive, like cameras. Therefore, our proposed system employ visible spectrum and infrared spectrum cameras, which are relatively chosen to be complementary, because the system must work well even under diﬃcult conditions, like poor illumination or bad-weather situations (such as dark, rain, fog). The monomodal systems are adapted to a single modality, either visible or infrared and even if they provide good recognition rates on the test set, these results could be improved by the combined processing of the visible and infrared information, which means in the frame of a bimodal system. The bimodal systems could take diﬀerent forms in function of the level at which the information is combined or fused. Thus, we propose three diﬀerent fusion systems: at the levels of features or at the ii level of SVM’s kernels, or even higher, at the level of matching-scores provided by the SVM. Each one of these systems improves classiﬁcation performances comparing to the monomodal systems. In order to ensure the adaptation of the system to the environmental conditions, within fusion schemes the kernels, the matching-scores and the features were weighted (with a sensor weighting coeﬃcient) according to the relative importance of the modality sensors. This allowed for better classiﬁcation performances. In the frame of the matching-scores fusion there is also the possibility to dynamically perform the adaptation of the weighting coeﬃcient to the context. In order to represent the obstacles’ images which have to be recognized by the Obstacle Recognition system, some features have been preferred to encode this information. These features are obtained in the features extraction module and they are wavelet features, statistical features, the coeﬃcients of some transforms, and others. Generally, the features extraction module is followed by a features selection one, in which the importance of these features is estimated and only the ones that are most relevant will be chosen to further represent the information. Diﬀerent features selection methods are tested and compared in order to evaluate the pertinence of each feature (and of each family of features) in relation to our objective of obstacle classiﬁcation. The pertinence of each vector constructed based on these features selection methods was ﬁrst evaluated by a KNN (k Nearest Neighbours) (with the number of neighbours k = 1) classiﬁer, due to the simplicity in its uploads/Litterature/ these-adiscant-apatean.pdf