In this Thesis, we developed algorithms that use visual informations to automatically perform, in real time, detection, recognition and categorisation of moving objects independently on the environmental conditions and with the best accuracy. To this end, we developed upon several concepts of computer vision, namely the identi\ufb01cation of the objects of interest in the whole visual scene (monocular or stereo), and their classi\ufb01cation. In the course of development, several approaches have been tested, including the detection of possible candidate by image segmentation with weak classi\ufb01ers and centroids, image segmentation algorithms enhanced by stereo information and reduction of noise, combination of popular features scale invariant (SIFT) combined with distance information. We developed two main categories of solutions associated with the type
\nof system used. With mobile cameras, we favourite the detection of known objects by scanning window; with \ufb01xed camera we use also foreground detection algorithms. In the case of foreground detection, detection rate and classi\ufb01cation rate increases if the quality of the objects extracted is high. We proposed methods to reduce the e\ufb00ects of shadow, illuminations, and repetitive moving objects. An inportant aspect we studied is the possibility to use a foreground detection by moveable camera. E\ufb03cient solutions are getting complex, but also the devices to compute the algorithms are more powerful, and in the recent years, GPU architecture o\ufb00er a big potential. We proposed a GPU implementation of an improved background management in order to increase the detection performance. In this Thesis we studied the detection and tracking of humans for applications such as the prevention of situation of risk (crossing street), and counting for analysis of tra\ufb03c. We studied these problems and explored the various aspects of human detection, group detection, and detection in crowded scenarios. However, in a generic environment, it is impossible to predict the con\ufb01guration of the objects that are captured by the camera. In these cases, we require to “abstract the concept” of an object. With this requirement in mind, we explored the property of stochastic methods and
\nshow that good classi\ufb01cation rates can be obtained provided that the training set is big enough. A \ufb02exible framework have to be able to detect moving regions and recognize the objects of interest. We developed a framework to manage the detection and classi\ufb01cation problem. Compared to other methods, the proposed systems o\ufb00er a \ufb02exible framework for objects detection and classi\ufb01cation, and can be used e\ufb03ciently in di\ufb00erent indoor and outdoors environments.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this Thesis, we developed algorithms that use visual informations to automatically perform, in real time, detection, recognition and categorisation of moving objects independently on the environmental conditions and with the best accuracy. To this end, we developed upon several concepts of computer vision, namely the identi\ufb01cation of the objects of interest in the whole […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,73,89,33,3],"tags":[1782,1791,14,1786,554,436,390],"class_list":["post-5847","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-computer-vision","category-nvidia-cuda","category-image-processing","category-paper","tag-computer-science","tag-computer-vision","tag-cuda","tag-image-processing","tag-nvidia-geforce-9800-gt","tag-nvidia-geforce-gtx-295","tag-thesis"],"views":1784,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5847","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5847"}],"version-history":[{"count":2,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5847\/revisions"}],"predecessor-version":[{"id":5848,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5847\/revisions\/5848"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5847"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5847"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}