Segmenting simply connected moving objects in a static scene

Bichsel, Martin

doi:10.1109/34.334396

Cited by 46 publications

(16 citation statements)

References 10 publications

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“…A popular approach to performing the automatic thresholding of difference images is to assume particular distribution models for the difference of image samples and the noise [1,4,9]. Instead our first method uses simple methods from robust statistics, and does not require any distribution assumptions.…”

Section: Automatic Thresholding Of Difference Imagesmentioning

confidence: 99%

Image Difference Threshold Strategies and Shadow Detection.

Rosin¹,

Ellis

1995

Procedings of the British Machine Vision Conference 1995

212

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The paper considers two problems associated with the detection and classification of motion in image sequences obtained from a static camera. Motion is detected by differencing a reference and the "current" image frame, and therefore requires a suitable reference image and the selection of an appropriate detection threshold. Several threshold selection methods are investigated, and an algorithm based on hysteresis thresholding is shown to give acceptably good results over a number of test image sets. The second part of the paper examines the problem of detecting shadow regions within the image which are associated with the object motion. This is based on the notion of a shadow as a semi-transparent region in the image which retains a (reduced contrast) representation of the underlying surface pattern, texture or grey value. The method uses a region growing algorithm which uses a growing criterion based on a fixed attenuation of the photometric gain over the shadow region, in comparison to the reference image.

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Section: Automatic Thresholding Of Difference Imagesmentioning

confidence: 99%

Image Difference Threshold Strategies and Shadow Detection.

Rosin¹,

Ellis

1995

Procedings of the British Machine Vision Conference 1995

212

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“…Background modeling and subtraction approaches have been extensively used and mainly rely on the use of color or luminance information (e.g. [5], [6]). [7] utilizes color and edge information in order to improve the quality and reliability of the results.…”

Section: Introductionmentioning

confidence: 99%

An Edge-Based Approach to Motion Detection

Sappa¹,

Dornaika²

2006

Computational Science – ICCS 2006

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Abstract. This paper presents a simple technique for motion detection in steady-camera video sequences. It consists of three stages. Firstly, a coarse moving edge representation is computed by a set of arithmetic operations between a given frame and two equidistant ones (initially the nearest ones). Secondly, non-desired edges are removed by means of a filtering technique. The previous two stages are enough for detecting edges corresponding to objects moving in the image plane with a dynamics higher than the camera's capture rate. However, in order to extract moving edges with a lower dynamics, a scheme that repeats the previous two stages at different time scales is performed. This temporal scheme is applied over couples of equidistant frames and stops when no new information about moving edges is obtained or a maximum number of iterations is reached. Although the proposed approach has been tested on human body motion detection it can be used for detecting moving objects in general. Experimental results with scenes containing movements at different speeds are presented.

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“…4. All input images are automatically segmented into foreground and background [1]. A level-set representation of the visual hull is computed to initialize the volume for the PDE evolution, Fig.…”

Section: Resultsmentioning

confidence: 99%