Background Subtraction Based on Random Superpixels Under Multiple Scales for Video Analytics

Fang, Weitao; Zhang, Tingting; Zhao, Chenqiu; Soomro, Danyal Badar; Taj, Rizwan; Hu, Haibo

doi:10.1109/access.2018.2846678

Cited by 12 publications

(12 citation statements)

References 20 publications

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“…4. After defining the superpixel scale-space pyramid, we adopt superpixel detector to obtain the saliency superpixel sets of each octave and record them as OS (1) , OS (2) , OS (3) , and OS (4) , respectively. The rule of detecting keysuperpixel is designed to be an idea that key-superpixel needs to be detected as a saliency superpixel in all octaves.…”

Section: ) Scale-space Saliency Superpixel Detectionmentioning

confidence: 99%

“…Thus, we gather the intersection of these sets as the key-superpixel set KS. KS = OS (1) ∩ OS (2) ∩ OS (3) ∩ OS (4) .…”

Section: ) Scale-space Saliency Superpixel Detectionmentioning

confidence: 99%

“…In this subsection, we compare SMMS to several popular algorithms including TP, 1 ERS, 2 SLIC, 3 LRW, 4 LSC, 5 and GMMS. 6 For these algorithms, the implementations are based on publicly available codes from their respective websites.…”

Section: Superpixel Segmentationmentioning

confidence: 99%

“…Image matching has been widely used recent years due to their potential applications in surveillance systems [1], visual tracking [2], object recognition [3], etc. Nowadays, a majority of available algorithms adopt keypoint based matching scheme to stitching images [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

2020

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This paper addresses an image matching methodology designed for correspondence problem in computer vision. Firstly, a novel superpixel segmentation model driven by spatially constrained Student's-t mixture model (SMM) is proposed. The tails of Student's t-distribution are heavier than that of traditional Gaussian distribution, therefore, SMM is more insensitive to outliers and noise. In this model, a spatially constraint term based on Markov random field (MRF) is designed, so that good boundary adherence and intensity homogeneity would be achieved. Next, by constructing an adaptive superpixel Gaussian filter and a superpixel salient detector, this paper establishes an innovative key-superpixel detection method by building a superpixel scale-space pyramid. Different from conventional keypoint based detection, two images could then be matched directly in a superpixel-to-superpixel manner. During the matching process, a combinatorial feature descriptor that merges color, shape, gradient and texture features is set up to distinguish each considered key-superpixel. One main advantage of this approach is that implementation time would be largely reduced by less matching demand for key-superpixels and few corresponding local features. Some experiments on datasets at the end would demonstrate a relatively better performance of our model.

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Section: ) Scale-space Saliency Superpixel Detectionmentioning

confidence: 99%

“…Thus, we gather the intersection of these sets as the key-superpixel set KS. KS = OS (1) ∩ OS (2) ∩ OS (3) ∩ OS (4) .…”

Section: ) Scale-space Saliency Superpixel Detectionmentioning

confidence: 99%

Section: Superpixel Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

2020

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“…4, August 2019 : 2715 -2724 2716 machine learning, deep learning, neural networks and data analytics used for the feature classification and knowledge discovery. Moreover, it has been seen that many existing researches have carried towards in the domain of video analytics with their advantages and limitations [6][7][8]. Also, various and different existing work of video analytics for object and event detection are discussed in review of literature section.…”

Section: Introductionmentioning

confidence: 99%

Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

Chandra

M²

2019

IJECE

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An effective video surveillance system is highly essential in order to ensure constructing better form of video analytics. Existing review of literatures pertaining to video analytics are found to directly implement algorithms on the top of the video file without much emphasis on following problems i.e. i) dynamic orientation of subject, ii)poor illumination condition, iii) identification and classification of subjects, and iv) faster response time. Therefore, the proposed system implements an analytical concept that uses depth-image of the video feed along with the original colored video feed to apply an algorithm for extracting significant information about the motion blob of the dynamic subjects. Implemented in MATLAB, the study outcome shows that it is capable of addressing all the above mentioned problems associated with existing research trends on video analytics by using a very simple and non-iterative process of implementation. The applicability of the proposed system in practical world is thereby proven.

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Superpixels-Guided Background Modeling Approach for Foreground Detection

Kalsotra

Arora

2022

Lecture Notes in Electrical Engineering

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Background Subtraction Based on Random Superpixels Under Multiple Scales for Video Analytics

Cited by 12 publications

References 20 publications

Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

Superpixels-Guided Background Modeling Approach for Foreground Detection

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