Comparative Analysis of GMM, KNN, and ViBe Background Subtraction Algorithms Applied in Dynamic Background Scenes of Video Surveillance System

Yasir, Maryam; Ali, Yossra H.

doi:10.30684/etj.v40i4.2154

Cited by 4 publications

(3 citation statements)

References 35 publications

(43 reference statements)

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“…This model performs better with respect to shadow and illumination change challenges [39]. GMM has been used frequently in the field of foreground detection and is still applied in many optimized versions [40]. The following are the general steps of the GMM algorithm: // k= number of clusters 2.…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

Yasir

Ali

2023

Iraqi Journal of Science

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Background subtraction is the dominant approach in the domain of moving object detection. Lots of research has been done to design or improve background subtraction models. However, there are a few well-known and state-of-the-art models that can be applied as a benchmark. Generally, these models are applied to different dataset benchmarks. Most of the time, choosing an appropriate dataset is challenging due to the lack of dataset availability and the tedious process of creating ground-truth frames for the sake of quantitative evaluation. Therefore, in this article, we collected local video scenes of a street and river taken by a stationary camera, focusing on dynamic background challenges. We presented a new technique for creating ground-truth frames using modeling, composing, tracking, and rendering each frame. Eventually, we applied three promising algorithms used in this domain: GMM, KNN, and ViBe, to our local dataset. Results obtained by quantitative evaluations revealed the effectiveness of our new technique for generating the ground-truth scenes to be benchmarked with the original scenes using a number of statistical metrics.

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Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

Yasir

Ali

2023

Iraqi Journal of Science

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“…In the following Tables 2-10 we illustrate the analytical metrics results of applying the benchmark background subtraction models SuBSENSE [16], ViBe [35], LOB-STER [36], GMM [15], KNN [37], KDE [18], Fuzzy Choquet Integral [38], Fuzzy Sugeno Integral [38], and Codebook [39] respectively on our local dataset videos, providing detailed results, the highest F1 is highlighted in bold. While Table 11 illustrates the average performance metrics of the forementioned models on the local dataset, the best result in each metric is highlighted in bold.…”

Section: Error Rate Pwc Fp Fn Tp Fn Tn Fpmentioning

confidence: 99%

Comparative Analysis of Background Subtraction Models Applied on a Local Dataset Using a New Approach for Ground-truth Generation

Yasir

Ali²

2022

Int. J. Recent Contrib. Eng. Sci. IT

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Abstract— Background subtraction is the dominant approach in the domain of moving object detection. Lots of research have been done to design or improve background subtraction models. However, there is a few well known and state of the art models which applied as a benchmark. Generally, these models are applied on different dataset benchmarks. Most of the time Choosing appropriate dataset is challenging due to the lack of datasets availability and the tedious process of creating the ground-truth frames for the sake of quantitative evaluation. Therefore, in this article we collected local video scenes for street and river taken by stationary camera focusing on dynamic background challenge. We presented a new technique for creating ground-truth frames using modelling, composing, tracking, and rendering each frame. Eventually we applied nine promising benchmark algorithms used in this domain on our local dataset. Results obtained by quantitative evaluations exposed the effectiveness of our new technique for generating the ground-truth scenes to be benchmarked with the original scenes using number of statistical metrics. Furthermore, results shows the outperformance of SuBSENSE model against other tested models.

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“…These models are capable of modeling variability in video sequences, which is why they have been widely used primarily in applications of video surveillance [7][8][9], moving object detection [10,11], human detection [12][13][14], and vehicle detection for traffic [15,16], among others.…”

Section: Introductionmentioning

confidence: 99%

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Romero-González,

Córdova-Esparza,

Terven

et al. 2024

Algorithms

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This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking.

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Comparative Analysis of GMM, KNN, and ViBe Background Subtraction Algorithms Applied in Dynamic Background Scenes of Video Surveillance System

Cited by 4 publications

References 35 publications

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

Comparative Analysis of Background Subtraction Models Applied on a Local Dataset Using a New Approach for Ground-truth Generation

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

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