Active contours for video object tracking using region, boundary and shape information

Állili, Madjid; Ziou, Djemel

doi:10.1007/s11760-007-0021-8

Cited by 26 publications

(11 citation statements)

References 35 publications

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“…A k, j may use rectangular (bounding box) [18], elliptical [10], or contour [1] representations. The number of estimated targets at frame k is denoted as n k , which are defined as {X k,1 , .…”

Section: Problem Definitionmentioning

confidence: 99%

A method for performance diagnosis and evaluation of video trackers

Nawaz

Ellis

Ferryman

2017

SIViP

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Several measures for evaluating multi-target video trackers exist that generally aim at providing 'end performance.' End performance is important particularly for ranking and comparing trackers. However, for a deeper insight into trackers' performance it would also be desirable to analyze key contributory factors (false positives, false negatives, ID changes) that (implicitly or explicitly) lead to the attainment of a certain end performance. Specifically, this paper proposes a new approach to enable a diagnosis of the performance of multi-target trackers as well as providing a means to determine the end performance to still enable their comparison in a video sequence. Diagnosis involves analyzing probability density functions of false positives, false negatives and ID changes of trackers in a sequence. End performance is obtained in terms of the extracted performance scores related to false positives, false negatives and ID changes. In the experiments, we used four state-of-the-art trackers on challenging real-world public datasets to show the effectiveness of the proposed approach.

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Section: Problem Definitionmentioning

confidence: 99%

A method for performance diagnosis and evaluation of video trackers

Nawaz

Ellis

Ferryman

2017

SIViP

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“…IoU and Dice are based on left Y axis, while MAD and HD are based on right Y axisance model and shape model could provide additional target boundary information. So the impact of the value τ variation is not significant.Figure 6reports the tracking performance using different values for τ , where we can see that the proposed method generates stable results when τ ∈[1,5].…”

mentioning

confidence: 95%

“…A better manner to cope with this problem is to use contours or silhouettes to represent the deformable targets. Recently, many contour-based tracking methods [1,[4][5][6]13,16,26,31,34] have been proposed to catch the detailed target shape information dynamically during tracking. Early work tends to use the parametric-based contour model [17] to track a set of marked points around the target.…”

Section: Introductionmentioning

confidence: 99%

Multiple cues-based active contours for target contour tracking under sophisticated background

Zhao

Chen

et al. 2016

Vis Comput

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In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a twolayer-based target appearance model that combines both discriminative pre-learned-based global layer and votingbased local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment. Electronic supplementary materialThe online version of this article (

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“…There are automatic techniques [13,14] for MFO segmentation from MCV shots. Very recently, a technique for foreground object segmentation from unconstrained video shots was proposed in [15].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Prominent moving object segmentation from moving camera video shots using iterative energy minimization

Chattopadhyay

Das

2014

SIViP

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Extraction of the moving foreground object from a given video shot is an important task for spatiotemporal analysis and content representation in many computer vision and digital video processing applications. We propose an iterative framework based on energy minimization, for segmenting the prominent moving foreground object efficiently from moving camera video (MCV) shots. The solution obtained using graph-cut for figure-ground classification is enhanced using features extracted over a set of neighboring frames. This is used to iteratively update the foreground and background probability (tri-) maps and hence the graph weights. The segmentation results from neighboring frames are integrated as constraints to iteratively guide the energy minimization process, for an efficient solution. The proposed framework is automatic and does not require any human interaction (neither initialization nor refinement). Our method outperforms recent state-of-the-art moving object segmentation techniques on benchmark datasets with MCV shots.

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Active contours for video object tracking using region, boundary and shape information

Cited by 26 publications

References 35 publications

A method for performance diagnosis and evaluation of video trackers

A method for performance diagnosis and evaluation of video trackers

Multiple cues-based active contours for target contour tracking under sophisticated background

Prominent moving object segmentation from moving camera video shots using iterative energy minimization

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