Background Subtraction Algorithm Based on Combination of Grabcut and Improved ViBe

Jiang, Shuihan; Gao, Yunbo; Wang, Changying; Qi, Junting; Cheng, Li; Zhang, Xiaojuan

doi:10.1145/3437802.3437811

Cited by 2 publications

(1 citation statement)

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“…However, ref. [49] identifies problems such as the ghost effect, sensitivity to shadows, or sensitivity to the target's movement speed.…”

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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“…However, ref. [49] identifies problems such as the ghost effect, sensitivity to shadows, or sensitivity to the target's movement speed.…”

Section: Introductionmentioning

confidence: 99%