Geodesic active regions for motion estimation and tracking

Paragios, Nikolaos; Deriche, Rachid

doi:10.1109/iccv.1999.791292

Cited by 107 publications

(68 citation statements)

References 18 publications

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“…The goal of this paper is to perform both segmentation and tracking to get the object O (i) t corresponding to the object O (i) t−1 of previous frame. Contrary to sequential segmentation techniques (Juan and Boykov, 2006;Kohli and Torr, 2005;Paragios and Deriche, 1999), we bring in object-level "observations". They may be of various kinds (e.g., obtained by a class-specific object detector, or motion/color detectors).…”

Section: Principle and Notationsmentioning

confidence: 99%

Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects With Graph Cuts

2008

Proceedings of the Third International Conference on Computer Vision Theory and Applications

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Abstract:This paper presents a new method to both track and segment multiple objects in videos using min-cut/max-flow optimizations. We introduce objective functions that combine low-level pixel-wise measures (color, motion), high-level observations obtained via an independent detection module (connected components of foreground detection masks in the experiments), motion prediction and contrast-sensitive contextual regularization. One novelty is that external observations are used without adding any association step. The minimization of these cost functions simultaneously allows "detection-before-track" tracking (track-to-observation assignment and automatic initialization of new tracks) and segmentation of tracked objects. When several tracked objects get mixed up by the detection module (e.g., single foreground detection mask for objects close to each other), a second stage of minimization allows the proper tracking and segmentation of these individual entities despite the observation confusion. Experiments on sequences from PETS 2006 corpus demonstrate the ability of the method to detect, track and precisely segment persons as they enter and traverse the field of view, even in cases of occlusions (partial or total), temporary grouping and frame dropping.

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Section: Principle and Notationsmentioning

confidence: 99%

Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects With Graph Cuts

2008

Proceedings of the Third International Conference on Computer Vision Theory and Applications

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“…For example Lowe [15] used salient features which are tracked using optical flow estimation, [16]. Paragios and Deriche [18] utilized optical flow as a further constraint to be minimized. Alternatively, boundary based optical flow may also be considered, e.g.…”

Section: Boundary Based Optical Flowmentioning

confidence: 99%

Tracking with Active Contours Using Dynamically Updated Shape Information

Chiverton

Xie

Mirmehdi

2008

Procedings of the British Machine Vision Conference 2008

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An active contour based tracking framework is described that generates and integrates dynamic shape information without having to learn a priori shape constraints. This dynamic shape information is combined with fixative photometric foreground model matching and background mismatching. Boundary based optical flow is also used to estimate the location of the object in each new video frame, incorporating Procrustes based shape alignment. Promising results under complex deformations of shape, varied levels of noise, and close-to-complete occlusion in the presence of complex textured backgrounds are presented.

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“…Finally, we would also like to mention [21] and [12] on shape reconstruction from unorganized points, and to the recent works [15] and [16], where a probability based geodesic active region model combined with classical gradient based active contour techniques is proposed.…”

Section: Introductionmentioning

confidence: 99%

An Active Contour Model without Edges

Chan

Vese

1999

Lecture Notes in Computer Science

487

538

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Abstract. In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. We minimize an energy which can be seen as a particular case of the so-called minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable.

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Geodesic active regions for motion estimation and tracking

Cited by 107 publications

References 18 publications

Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects With Graph Cuts

Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects With Graph Cuts

Tracking with Active Contours Using Dynamically Updated Shape Information

An Active Contour Model without Edges

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