Polar snakes: a fast and robust parametric active contour model

We present in this paper a way to perform a fast and robust image segmentation and to track a contour along a sequence of images. Our approach is based on a dynamic deformable model. More precisely, we revisit the physics basedmodel proposed in [1] to show the benefit of using a polar description to model the contour, in particular to cope with the well-known initialization problem. Indeed, we show that this way to proceed leads to diagonal and constant matrices in the equations of the snake evolution yielding therefore to a faster algorithm. Experimental results on image segmentation and contour tracking validate the efficiency of this new formulation. Index Terms — Active contour model, polar description, segmentation, contour tracking. 1

Suivi robuste d'objets en temps-réel : une approche hybride 2D-3D

Dans cet article, nous présentons une méthode originale de suivi d'objets complexes approximativement modélisés par un polyèdre. L'approche repose sur l'estimation du mouvement de l'objet dans l'image, ainsi que sur un calcul de pose. La méthode proposée permet un suivi fiable et robuste en temps réel comme le montrent les résultats présentés

Knowledge modelling for the motion detection task

In this article knowledge modelling at the knowledge level for the task of moving objects detection in image sequences is introduced. Three items have been the focus of the approach: (1) the convenience of knowledge modelling of tasks and methods in terms of a library of reusable components and in advance to the phase of operationalization of the primitive inferences; (2) the potential utility of looking for inspiration in biology; (3) the convenience of using these biologically inspired problem-solving methods (PSMs) to solve motion detection tasks. After studying a summary of the methods used to solve the motion detection task, the moving targets in indefinite sequences of images detection task is approached by means of the algorithmic lateral inhibition (ALI) PSM. The task is decomposed in four subtasks: (a) thresholded segmentation; (b) motion detection; (c) silhouettes parts obtaining; and (d) moving objects silhouettes fusion. For each one of these subtasks, first, the inferential scheme is obtained and then each one of the inferences is operationalized. Finally, some experimental results are presented along with comments on the potential value of our approach

Tracking motion and intensity variations using hierarchical 2-D mesh modeling for synthetic object transfiguration

We propose a method for tracking the motion and intensity variations of a 2-D mildly deformable image object using a hierarchical 2-D mesh model. The proposed method is applied to synthetic object transfiguration, namely, replacing an object in a real video clip with another synthetic or natural object via digital postprocessing. Successful transfiguration requires accurate tracking of both motion and intensity (contrast and brightness) variations of the object-to-be-replaced so that the replacement object can be rendered in exactly the same way from a single still picture. The proposed method is capable of tracking image regions corresponding to scene objects with nonplanar and/or mildly deforming surfaces, accounting for intensity variations, and is shown to be effective with real image sequences. © 1996 Academic Press, Inc

Pedestrian head detection using automatic scale selection for feature detection and statistical edge curvature analysis

In this report we focus on pedestrian head detection and tracking in video sequences. The task is not trivial in real and complex scenarios where the deformation induced by the perspective field requires a multi-scale analy- sis. Multi-scale shape models for the human head are considered to identify the correct size of the region of interest. Anisotropic diffusion is used as a pre-processing step and edge detection is performed using an automatic scale selection process. A non parametric statistical description is given for the edge curvature and detection is performed by means of goodness-of-fit tests. The head detector is used as a validation tool in a correlation-based tracker. The local maxima of the correlation matrix are analyzed. Tracking is performed associating the displacement vector of the target with that local maximum which maximizes the goodness-of-fit with the distribution of the edge curvature of the head

Moving object detection and tracking in wavelet compressed video

Cataloged from PDF version of article.In many surveillance systems the video is stored in wavelet compressed form. An algorithm for moving object and region detection in video that is compressed using a wavelet transform (WT) is developed. The algorithm estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference. The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. This leads to a computationally efficient method and a system compared to the existing motion estimation methods. In a second aspect, size and locations of moving objects and regions in video is estimated from the wavelet coefficients of the current image, which differ from the estimated background wavelet coefficients. This is possible because wavelet coefficients of an image carry both frequency and space information. In this way, we are able to track the detected objects in video. Another feature of the algorithm is that it can determine slowing objects in video. This is important in many practical applications including highway monitoring, queue control, etc.Töreyin, Behçet UğurM.S

Design of a person tracking algorithm for the intelligent room

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 101-102).by Gregory Andrew Klanderman.M.S

Vision dynamique pour la navigation d'un robot mobile

Les travaux présentés dans cette thèse concernent l’étude des fonctionnalités visuelles sur des scènes dynamiques et ses applications à la robotique mobile. Ces fonctionnalités visuelles traitent plus précisément du suivi visuel d’objets dans des séquences d’images. Quatre méthodes de suivi visuel ont été étudiées, dont trois ont été développées spécifiquement dans le cadre de cette thèse. Ces méthodes sont : (1) le suivi de contours par un snake, avec deux variantes permettant son application à des séquences d’images couleur ou la prise en compte de contraintes sur la forme de l’objet suivi, (2) le suivi de régions par différences de motifs, (3) le suivi de contours par corrélation 1D, et enfin (4) la méthode de suivi d’un ensemble de points, fondée sur la distance de Hausdorff, développée lors d’une thèse précédente. Ces méthodes ont été analysées pour différentes tâches relatives à la navigation d’un robot mobile; une comparaison dans différents contextes a été effectuée, donnant lieu à une caractérisation des cibles et des conditions pour lesquelles chaque méthode donne de bons résultats. Les résultats de cette analyse sont pris en compte dans un module de planification perceptuelle, qui détermine quels objets (amers plans) le robot doit suivre pour se guider le long d’une trajectoire. Afin de contrôler l’exécution d’un tel plan perceptuel, plusieurs protocoles de collaboration ou d’enchaînement entre méthodes de suivi visuel ont été proposés. Finalement, ces méthodes, ainsi qu’un module de contrôle d’une caméra active (site, azimut, zoom), ont été intégrées sur un robot. Trois expérimentations ont été effectuées: a) le suivi de route en milieu extérieur, b) le suivi de primitives pour la navigation visuelle en milieu intérieur, et c) le suivi d’amers plans pour la navigation fondée sur la localisation explicite du robot. ABSTRACT : The work presented on this thesis concerns the study of visual functionalities over dynamic scenes and their applications to mobile robotics. These visual functionalities consist on visual tracking of objects on image sequences. Four methods of visual tracking has been studied, from which tree of them has been developed specifically for the context of this thesis. These methods are: (1) snakes contours tracking, with two variants, the former, to be able to applying it to a sequence of color images and the latter to consider form constraints of the followed object, (2) the tracking of regions by templates differences, (3) contour tracking by 1D correlation, and (4) the tracking method of a set of points, based on Hausdorff distance, developed on a previous thesis. These methods have been analyzed for different tasks, relatives to mobile robot’s navigation. A comparison for different contexts has been done, given to a characterization of objects and conditions for which each method gives the best results. Results from this analysis has been take into account on a perceptual planification module, that determines which objects (plane landmarks) must be tracked by the robot, to drive it over a trajectory. In order to control the execution of perceptual plan, a lot of collaboration or chaining protocols have been proposed between methods. Finally, these methods and a control module of an active camera (pan, tilt, zoom), has been integrated on a robot. Three experiments have been done: a) road tracking over natural environments, b) primitives tracking for visual navigation over human environments and c) landmark tracking for navigation based on explicit localization of robo