VIDEO OBJECT TRACKING USING FOREGROUND MODELS

Improvement of object tracking techniques using grab cut an foreground modelsThis Master Thesis present an approach to Video Object Tracking segmentation using foreground models. For the video sequences analysed, the foreground and the background have been modelled using Spatial Colour Gaussian Mixture Models (SCGMMs). SCGMMs are Gaussian Models which describes the foreground and the background using five components in colour and spatial domains. In order to have a better result in the segmentation process, the Gaussian Models computed for each frame are passed to the next frame using a tacking technique that helps in the individuation of the object in foreground alone the sequence. Using the location provided by the tracking, the Gaussian Mixture Model for the background is computed only in the close region around the object in foreground allowing in this way a better modelling of the region. The Thesis is structure as follows: after a presentation of the study of the State of the Art where the techniques for tracking and segmentation are presented, there is the presentation of the method proposed. At the end there is a Chapter that describes the results obtained and some conclusions and a Chapter which presents some future developments

Foreground objects segmentation for moving camera scenarios based on SCGMM

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.Peer ReviewedPostprint (published version

Foreground objects segmentation for moving camera scenarios based on SCGMM

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor sce narios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the objectPeer Reviewe

Foreground objects segmentation for moving camera scenarios based on SCGMM

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.Peer Reviewe

Foreground objects segmentation for moving camera scenarios based on SCGMM

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor sce narios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the objectPeer Reviewe