Motion detection with nonstationary background

Ying, Rendong; Chua, Chin-Seng; Ho, Yeong-Khing

doi:10.1007/s00138-002-0091-0

Cited by 59 publications

(38 citation statements)

References 15 publications

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“…Pixels which do not match their own background model are compared to the models in a small neighborhood. A similar, but more comprehensive, approach is taken by Ren's "Spatially Distributed Gaussians" (SDG) [16] to account for global background transformations caused by a moving camera. This system first registers global image features to estimate a global translation between the background model and observed image.…”

Section: Previous Workmentioning

confidence: 99%

“…Foreground components are introduced when regions of pixels appear which have a low probability under the mixture model. Such regions are taken to be generated by new foreground objects entering the scene, and appear in the support map as regions in which a high density of pixels have been labeled as unassigned by equation (16).…”

Section: Introducing Foreground Componentsmentioning

confidence: 99%

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A spatially distributed model for foreground segmentation

Dickinson

Hunter

Appiah

2009

Image and Vision Computing

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Foreground segmentation is a fundamental first processing stage for vision systems which monitor real-world activity. In this paper we consider the problem of achieving robust segmentation in scenes where the appearance of the background varies unpredictably over time. Variations may be caused by processes such as moving water, or foliage moved by wind, and typically degrade the performance of standard per-pixel background models.Our proposed approach addresses this problem by modeling homogeneous regions of scene pixels as an adaptive mixture of Gaussians in color and space. Model components are used to represent both the scene background and moving foreground objects. Newly observed pixel values are probabilistically classified, such that the spatial variance of the model components supports correct classification even when the background appearance is significantly distorted. We evaluate our method over several challenging video sequences, and compare our results with both per-pixel and Markov Random Field based models. Our results show the effectiveness of our approach in reducing incorrect classifications.

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Section: Previous Workmentioning

confidence: 99%

Section: Introducing Foreground Componentsmentioning

confidence: 99%

A spatially distributed model for foreground segmentation

Dickinson

Hunter

Appiah

2009

Image and Vision Computing

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“…However, we want to point out that a similar algorithm, presented in Ref. 18 can be viewed as a generalization of this idea for nonvideo surveillance applications. This background subtraction algorithm has been applied to scene modeling using sequences from hand-held cameras.…”

Section: Correctmentioning

confidence: 99%

Importance of detection for video surveillance applications

et al. 2008

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Abstract. Though it is the first step of a real video surveillance application, detection has received less attention than tracking in research on video surveillance. We show, however, that the majority of errors in the tracking task are due to wrong detection. We show this by experimenting with a multi object tracking algorithm based on a Bayesian framework and a particle filter. This algorithm, which we have named iTrack, is specifically designed to work in practical applications by defining a statistical model of the object appearance to build a robust likelihood function. Likewise, we present an extension of a background subtraction algorithm to deal with active cameras. This algorithm is used in the detection task to initialize the tracker by means of a prior density. By defining appropriate performance metrics, the overall system is evaluated to elucidate the importance of detection for video surveillance applications.

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“…For example, many of the state-of-the-art techniques [8,16,22] assume a static camera, and are unsuitable for video shot with hand-held cameras or from moving platforms (as in the robot example). The conventional approach to background subtraction in the presence of ego-motion is to first explicitly [17], or approximately [19], compensate for the camera motion, and then rely on stationary camera background subtraction techniques. Accurate compensation of ego-motion is, however, cumbersome and can be quite difficult when the background is itself dynamic.…”

Section: Introductionmentioning

confidence: 99%