Adaptive background subtraction using fuzzy logic

Sivabalakrishnan, M.; Manjula, D.

doi:10.1504/ijmis.2010.039239

Cited by 13 publications

(9 citation statements)

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“…They concentrated on two features, one is the colour and the other is the depth and used RGB-D benchmark dataset. Sivabalakrishnan and Manjula (2010) proposed a fuzzy inference method for adapting the background model. Chen et al (2007) modelled the background based on contrast histogram.…”

Section: Related Workmentioning

confidence: 99%

An optimised background modelling for efficient foreground extraction

Revathi¹,

Jeganathan

Sivagami

2017

IJHPCN

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Nowadays, analysing videos from a surveillance system in real-time is very important for resolving the security related social issues. Foreground extraction and object detection is a vital task in video analysis. In the proposed methods background, modelling is treated as an optimisation problem and solved using particle swarm optimisation. The background is modelled at regular intervals of time for adapting the changes in the environment. Then the background subtraction is applied to the current frame with the corresponding background modelled frame to extract the foreground. Added to it the optical flow applied image is compared with the foreground extracted image to avoid the false positives (FP) and false negatives (FN). This proposed foreground extraction technique for real-time videos gives results better than the previous algorithms with respect to the quality of extraction and space complexity. L. Jeganathan is a Professor and the Dean of the School of Computing Science and Engineering, VIT University, India. Also, he heads the research team of theoretical computer science with the motivation of designing the effective computational models using cellular automata. His research interest includes theoretical computer science, natural computing and bio-inspired computing. He has completed his PhD in IIT Madras in 2009. He has published many national and international conference and journal papers.

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Section: Related Workmentioning

confidence: 99%

An optimised background modelling for efficient foreground extraction

Revathi¹,

Jeganathan

Sivagami

2017

IJHPCN

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“…Then supporting the background subtraction methodology, a BFSD target detection rule is projected [16]. This paper shows the effectiveness and robust nature of fuzzy logic in performing background subtraction in dynamic environments [17].…”

Section: Related Workmentioning

confidence: 99%

“…The difference is compared to a threshold value. Choosing the threshold properly is important as if it is too small, it will produce a lot of false change points, and if the threshold choice is too large, it will reduce the scope of changes in movement [17]. Then the image is converted to binary, and only the large blobs are opened to get the count.…”

Section: Algorithm For Vehicle Count and Density Of Trafficmentioning

confidence: 99%

Agent Based Intelligent Traffic Management System for Smart Cities

Sandhu¹,

Jain²,

Gaurav³

et al. 2015

IJSEIA

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“…It is the most popular approach in video surveillance applications because it is a computationally efficient technique and offers relative ease in obtaining background images for static surveillance cameras. [12] In practice, camera noise and regions in which the object is of the same color as the background make the separation of foreground objects and background more difficult. There are a few post processing filters that can remove obvious errors like small clutter regions.…”

Section: Technique Of Background Subtractionmentioning

confidence: 99%

Fuzzy Rule-based Classification of Human Tracking and Segmentation using Color Space Conversion

Sivabalakrishnan¹,

Manjula²

2010

IJAIA

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In this study we are proposing a fuzzy-based rule system for tracking people by using simple tracking algorithms. A simple hue, saturation, and value (HSV) histogram-based color model was used to develop our system. We started by separating the moving object region from the background region by comparing the current frame with the constructed background image. In this paper, as a first step we obtained a motion image through the acquisition and segmentation of video sequences. In this situation where object shadows appear in the background region, a pre-processing median filter is applied to the input image to reduce the shadow effect, before identifying major blobs. The second step includes generating a set of blobs from detected varied regions in each image sequence. When objects are closer the segmented object gets rejected and is thereby detected as a single object. This error can be rectified by using fuzzy logic. .

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Adaptive background subtraction using fuzzy logic

Cited by 13 publications

References 0 publications

An optimised background modelling for efficient foreground extraction

An optimised background modelling for efficient foreground extraction

Agent Based Intelligent Traffic Management System for Smart Cities

Fuzzy Rule-based Classification of Human Tracking and Segmentation using Color Space Conversion

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