Effective hierarchical background modeling and foreground detection in surveillance systems

Armanfard, Narges; Komeili, Majid; Valizadeh, Morteza; Kabir, Ehsanollah

doi:10.1109/csicc.2009.5349318

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…LBP has been used by Heikkila and Pietaikinen (2006) to model the history of each pixel in any block. Armanfard et al (2009b) tried to reduce the size of the model.…”

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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“…LBP has been used by Heikkila and Pietaikinen (2006) to model the history of each pixel in any block. Armanfard et al (2009b) tried to reduce the size of the model.…”

Section: Methodsmentioning

confidence: 99%

Using Multi-Scale Filtering to Initialize a Background Extraction Model

Davarpanah

Khalid

Nan

et al. 2012

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…It has acceptable performance in texture classification, fabric defect detection and moving region detection. In this method, local features which consider the pixel's neighboring pixels are assigned as its texture features (Armanfard et al, 2009b). LBP was used by (Heikkila and Pietaikinen, 2006) to model the history of each pixel in any block.…”

Section: Related Workmentioning

confidence: 99%

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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