75,004 research outputs found
MRF-based background initialisation for improved foreground detection in cluttered surveillance videos
Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible. In this paper, we propose a method capable of robustly estimating the background and detecting regions of interest in such environments. In particular, we propose to extend the background initialisation component of a recent patch-based foreground detection algorithm with an elaborate technique based on Markov Random Fields, where the optimal labelling solution is computed using iterated conditional modes. Rather than relying purely on local temporal statistics, the proposed technique takes into account the spatial continuity of the entire background. Experiments with several tracking algorithms on the CAVIAR dataset indicate that the proposed method leads to considerable improvements in object tracking accuracy, when compared to methods based on Gaussian mixture models and feature histograms
Fast detecting and tracking of moving objects in video scenes
18 pages. Quelques films de résultats sont disponible sur: http://www.ceremade.dauphine.fr/~pelletieIn this article we present a new method for detecting textured moving objects. Based on a known background estimation and a fixed camera, the algorithm is able to detect moving objects and locates them at video rate, moreover this method is used for object tracking purposes. Our method is multi-step: First, we use level lines to detect pixels of the background which are occluded by moving object. Then, we use an a contrario model as general framework to make an automatic clustering. Thus the moving objects are detected as regions and not only as pixels, eventually we correct this region to better fit the moving object. Experimental results show that the algorithm is very robust to noise and to the quality of the background estimation (e.g. ghosts). The algorithm has been successfully tested in video sequences coming from different databases, including indoor and outdoor sequences
Modal-Graph 3D Shape Servoing of Deformable Objects with Raw Point Clouds
Deformable object manipulation (DOM) with point clouds has great potential as
non-rigid 3D shapes can be measured without detecting and tracking image
features. However, robotic shape control of deformable objects with point
clouds is challenging due to: the unknown point-wise correspondences and the
noisy partial observability of raw point clouds; the modeling difficulties of
the relationship between point clouds and robot motions. To tackle these
challenges, this paper introduces a novel modal-graph framework for the
model-free shape servoing of deformable objects with raw point clouds. Unlike
the existing works studying the object's geometry structure, our method builds
a low-frequency deformation structure for the DOM system, which is robust to
the measurement irregularities. The built modal representation and graph
structure enable us to directly extract low-dimensional deformation features
from raw point clouds. Such extraction requires no extra point processing of
registrations, refinements, and occlusion removal. Moreover, to shape the
object using the extracted features, we design an adaptive robust controller
which is proved to be input-to-state stable (ISS) without offline learning or
identifying both the physical and geometric object models. Extensive
simulations and experiments are conducted to validate the effectiveness of our
method for linear, planar, tubular, and solid objects under different settings
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