Multiscale background modelling and segmentation

Ćulibrk, Dubravko; Crnojević, Vladimir; Antic, Borislav

doi:10.1109/icdsp.2009.5201193

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…The Z -score is utilized to detect the outlines in the frame. The Z-score value for each pixel is calculated by dividing the distance between values of that pixel in the filtered image and a mean value of pixels in the filtered image over the Mean Absolute Distance (MAD) (Culibrk et al, 2009). MAD is defined as Eq.…”

Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

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Problem statement: Probability-based methods which usually work based on the saved history of each pixel are utilized severally in extracting a background image for moving detection systems. Probability-based methods suffer from a lack of information when the system first begins to work. The model should be initialized using an alternative accurate method. Approach: The use of a nonparametric filtering to calculate the most probable value for each pixel in the initialization phase can be useful. In this study a complete system to extract an adaptable gray scale background image is presented. It is a probability-based system and especially suitable for outdoor applications. The proposed method is initialized using a multi-scale filtering method. Results: The results of the experiments certify that not only the quality of the final extracted background is about 10% more accurate in comparison to four recent re-implemented methods, but also the time consumption of the extraction are acceptable. Conclusion: Using multi-scale filtering to initialize the background model and to extract the background using a probability-based method proposes an accurate and adaptable background extraction method which is able to handle sudden and large illumination changes.

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Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

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“…In [7], the authors proposed the use of a multi-scale background modeling and foreground segmentation approach proposed in [4] as an efficient attention model driven by both motion and static cues, which adheres to the principles reported in [26]. The model employs the principles of multi-scale processing, cross-scale motion consistency, outlier detection, and temporal coherence.…”

Section: Saliency Motion and Attentionmentioning

confidence: 98%

Saliency and Attention for Video Quality Assessment

Ćulibrk

2016

From Human Attention to Computational Attention

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Recent years have witnessed an explosion of visual and multimedia applications across the globe. Digital television and other home entertainment applications; multimedia sharing platforms such as YouTube, Flickr, and Panoramio; social networking applications such as Facebook; mobile multimedia applications; personal multimedia collections; immersive multimedia and virtual reality applications; video tele-conferencing; gaming; and educational multimedia presentations are all examples of multimedia applications that have become an integral part of our lives.Our increased ability to transmit large amounts of data, through both land and wireless networks, has led to rising increase in the quality of experience that people get and expect from multimedia applications and services. Since the consumers of the multimedia content are ultimately humans, it is vital to be able to efficiently measure and detect the quality of the final product delivered to the users for large amounts of multimedia data. The problem is made more complex by the increasing share of multimedia generated by users, as opposed to the professional media-producing companies. At the time of writing, 300 h of video are uploaded to YouTube (the leading Internet video site) every minute. In fact the amount of video uploaded to YouTube in a month surpasses all the content produced by three This work supported in part by FP7 QoSTREAM (no. 295220) and COST SoftStat IC0702 projects.

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“…The proposed method is an improved version of an existent algorithm (Culibrk et al, 2009). It is a hybrid algorithm.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Similar to the algorithm of Culibrk et al (2009), two dimensional Negative Mexican Filter is used. While they apply the filter four times, our results show after applying twice of the filter over the square neighborhood 14 and 7 pixels wide respectively, the expected result are achieved.…”

Section: Spatial Filteringmentioning

confidence: 99%

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A Block-Based Multi-Scale Background Extraction Algorithm

Davarpanah¹

2010

Journal of Computer Science

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Problem statement: To extract the moving objects, vision-based surveillance systems subtract the current image from a predefined background image. The efficiency of these systems mainly depends on accuracy of the extracted background image. It should be able to adapt to the changes continuously. In addition, especially in real-time applications the time complexity of this adaptation is a critical matter. Approach: In this study, to extract an adaptive background, a combination of blocking and multi-scale methods is presented. Because of being less sensitive to local movements, block-based techniques are proper to control the non-stationary objects' movements, especially in outdoor applications. They can be useful to reduce the effect of these objects on the extracted background. We also used the blocking method to intelligently select the regions which the temporal filtering has to be applied on. In addition, an amended multi-scale algorithm is introduced. This algorithm is a hybrid algorithm, a combination of some nonparametric and parametric filters. It uses a nonparametric filter in the spatial domain to initiate two primary backgrounds. In continue two adapted two-dimensional filters will be used to extract the final background. Results: The qualitative and quantitative results of our experiments certify not only the quality of the final extracted background is acceptable, but also its time consumption is approximately half in compare to the similar methods. Conclusion: Using Multi scaling filtering and applying the filters just to some selected nonoverlapped blocks reduce the time consumption of the extracting background algorithm.

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Multiscale background modelling and segmentation

Cited by 11 publications

References 14 publications

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Saliency and Attention for Video Quality Assessment

A Block-Based Multi-Scale Background Extraction Algorithm

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