A Self-adaptive CodeBook (SACB) model for real-time background subtraction

Shah, Munir; Deng, Jeremiah D.; Woodford, Brendon J.

doi:10.1016/j.imavis.2015.02.001

Cited by 29 publications

(10 citation statements)

References 8 publications

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“…Motivated by the work of [9], which has proposed the statistical parameter estimation method in YCbCr color space, we propose a multispectral self-adaptive method for automatically optimal parameter selection. That is to say, those parameters do not need to be obtained by burdensome experiments, but to be estimated from the data themselves statistically, which can help to save a lot of efforts and time.…”

Section: Multispectral Self-adaptive Codebookmentioning

confidence: 99%

“…Earlier studies usually exploit the background subtraction using visible light cameras, mainly RGB, or transferring it to other color model, like YCbCr [9], Lab [3] or YUV (Y, Luminance; U, Chrominance; V, Chroma) color space [10]. Commonly, the methods developed for visible light cameras are particularly sensitive to low light conditions and specular reflections, especially in an outdoor environment.…”

Section: Introductionmentioning

confidence: 99%

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Extended Codebook with Multispectral Sequences for Background Subtraction

Liu

Ruichek

El-Bagdouri

2019

Sensors

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: The Codebook model is one of the popular real-time models for background subtraction. In this paper, we first extend it from traditional Red-Green-Blue (RGB) color model to multispectral sequences. A self-adaptive mechanism is then designed based on the statistical information extracted from the data themselves, with which the performance has been improved, in addition to saving time and effort to search for the appropriate parameters. Furthermore, the Spectral Information Divergence is introduced to evaluate the spectral distance between the current and reference vectors, together with the Brightness and Spectral Distortion. Experiments on five multispectral sequences with different challenges have shown that the multispectral self-adaptive Codebook model is more capable of detecting moving objects than the corresponding RGB sequences. The proposed research framework opens a door for future works for applying multispectral sequences in moving object detection.

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Section: Multispectral Self-adaptive Codebookmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended Codebook with Multispectral Sequences for Background Subtraction

Liu

Ruichek

El-Bagdouri

2019

Sensors

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“…On the other hand, Kim et al [13] have proposed a codebook method which uses a clustering technique to model the background and initializes codewords of codebooks to store background states, where codewords are a series of key color values. Next, Shah et al [14] designed a Self-Adaptive CodeBook (SACB) background model. This model is a block-based structure using the close proximity of local neighborhoods.…”

Section: Theoretical Reviewmentioning

confidence: 99%

Automatic foreground detection based on KDE and binary classification

Lahraichi

Housni

Mbarki

2019

IJEECS

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In the recent decades, several methods have been developed to extract moving objects in the presence of dynamic background. However, most of them use a global threshold, and ignore the correlation between neighboring pixels. To address these issues, this paper presents a new approach to generate a probability image based on Kernel Density Estimation (KDE) method, and then apply the Maximum A Posteriori in the Markov Random Field (MAP-MRF) based on probability image, so as to generate an energy function, this function will be minimized by the binary graph cut algorithm to detect the moving pixels instead of applying a thresholding step. The proposed method was tested on various video sequences, and the obtained results showed its effectiveness in presence of a dynamic scene, compared to other background subtraction models.

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“…In contrast, the AD approach formulates the problem as a one-class classification [14], in which the goal is to classify (not a live-map-image-pair but) a query live image as a "change" or a "no-change" [10], [11], [15]- [23], with respect to an offline pretrained normal model. Unlike PC approaches, this formulation facilitates the utilization of compressed normal models such as bag-of-visual-features (BoVFs) [10], [11], [15], 3D/landmark/grid maps [16]- [20], compact manifold learning [21]- [23], and autoencoders (AEs) [2]. Early studies employed one-class support vector machines [14], or support vector data description [24].…”

Section: B Anomaly Detection (Ad)mentioning

confidence: 99%

Fault-Diagnosing SLAM for Varying Scale Change Detection

Sugimoto¹,

Yamaguchi²,

Kanji³

2019

Preprint

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In this paper, we present a new fault diagnosis (FD) -based approach for detection of imagery changes that can detect significant changes as inconsistencies between different sub-modules (e.g., self-localizaiton) of visual SLAM. Unlike classical change detection approaches such as pairwise image comparison (PC) and anomaly detection (AD), neither the memorization of each map image nor the maintenance of up-to-date place-specific anomaly detectors are required in this FD approach. A significant challenge that is encountered when incorporating different SLAM sub-modules into FD involves dealing with the varying scales of objects that have changed (e.g., the appearance of small dangerous obstacles on the floor). To address this issue, we reconsider the bag-of-words (BoW) image representation, by exploiting its recent advances in terms of self-localization and change detection. As a key advantage, BoW image representation can be reorganized into any different scaling by simply cropping the original BoW image. Furthermore, we propose to combine different self-localization modules with strong and weak BoW features with different discriminability, and to treat inconsistency between strong and weak self-localization as an indicator of change. The efficacy of the proposed approach for FD with/without AD and/or PC was experimentally validated.

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A Self-adaptive CodeBook (SACB) model for real-time background subtraction

Cited by 29 publications

References 8 publications

Extended Codebook with Multispectral Sequences for Background Subtraction

Extended Codebook with Multispectral Sequences for Background Subtraction

Automatic foreground detection based on KDE and binary classification

Fault-Diagnosing SLAM for Varying Scale Change Detection

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