Statistical background modeling for non-stationary camera

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

doi:10.1016/s0167-8655(02)00210-6

Cited by 71 publications

(37 citation statements)

References 6 publications

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“…Ren et al [110] extended a statistical background modeling technique to cope with a non-stationary camera. The current image is registered to the estimated background image using an affine or projective transformation.…”

Section: Background Modelingmentioning

confidence: 99%

Image change detection algorithms: a systematic survey

Radke

Andra

Al-Kofahi

et al. 2005

IEEE Trans. on Image Process.

1,601

790

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Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

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Section: Background Modelingmentioning

confidence: 99%

Image change detection algorithms: a systematic survey

Radke

Andra

Al-Kofahi

et al. 2005

IEEE Trans. on Image Process.

1,601

790

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show abstract

“…Solution to the problem of detection and monitoring ground moving objects is given in a large number of papers, for example, (Hayman & Eklundh, 2001;Ren et al, 2003;Uemura et al, 2008;Borshukov et al . ;Ke & Kanade, 2001;Tao et al, 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

Ensuring the Accuracy of Traffic Monitoring Using Unmanned Aerial Vehicles Vision Systems

Kim¹,

Bodunkov²,

Cherkezov³

2015

MAS

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The paper is dedicated to the organization of traffic monitoring using unmanned aerial vehicles (UAV). It is demonstrated that the monitoring of traffic makes high demands on the accuracy of determining the position of the vehicle on the road.It is assumed that the purpose of monitoring is detecting specific situations, which may include accident, in particular, car collision; traffic accidents, reducing the bandwidth of the road section; movement of the vehicle, being a threat to other road users.Detection of such situations requires assessment of the following at the received images:• Vehicles position relatively to the road markings;• Vehicles position relatively to each other;• Vehicles speed. Review of the literature showed that the existing tools for tracking ground objects movements provide sufficiently accurate assessment of the vehicles coordinates at the images. Thus, an important issue is the estimation of vehicle position with respect to the road, i.e. in the ground coordinate system of the road. Different options of the vehicle position assessment relatively the road are researched.Evaluation of the content and accuracy of the standard UAV navigation system showed that the option of monitoring based on the use of UAV position assessment relative to the ground coordinate system and the vehicles is non-implementable because of lack of precision at the standard navigation system, including, corrected using the satellite navigation system. Assessing the position of the vehicle relative to the roadside is proposed to be made using image processing algorithms, particularly the contour lines highlighting and the Hough algorithm for straight segments highlighting.The research shows that this option based on direct assessment of the situation with respect to the vehicle position on the road image is physically implementable.

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“…Other approaches, that aim at overcoming the single plane assumption, do not involve the definition of constraints [1,11,13,14,19]. In [13], the authors align each frame with the reference mosaic repeatedly: at each iteration, the region that best fits the current registration is used to determine a plane that is excluded from the successive registrations steps.…”

Section: Related Workmentioning

confidence: 99%

Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

Romanoni

Matteucci

Sorrenti

2013

Machine Vision and Applications

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Background subtraction is the classical approach to differentiate moving objects in a scene from the static background when the camera is fixed. If the fixed camera assumption does not hold, a frame registration step is followed by the background subtraction. However, this registration step cannot perfectly compensate camera motion, thus errors like translations of pixels from their true registered position occur. In this paper, we overcome these errors with a simple, but effective background subtraction algorithm that combines Temporal and Spatio-Temporal approaches. The former models the temporal intensity distribution of each individual pixel. The latter classifies foreground and background pixels, taking into account the intensity distribution of each pixels' neighborhood. The experimental results show that our algorithm outperforms the state-of-the-art systems in the presence of jitter, in spite of its simplicity.

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Statistical background modeling for non-stationary camera

Cited by 71 publications

References 6 publications

Image change detection algorithms: a systematic survey

Image change detection algorithms: a systematic survey

Ensuring the Accuracy of Traffic Monitoring Using Unmanned Aerial Vehicles Vision Systems

Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

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