Moving object detection based on ViBe long-term background modeling

doi:10.1016/j.dsp.2024.104976

Moving object detection based on ViBe long-term background modeling

2025-03-01

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DOI：10.1016/j.dsp.2024.104976

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The ViBe algorithm is a motion target detection algorithm based on a static background. To address the issue where static foreground objects mistakenly incorporated into the background can destroy the real background, leading to poor target detection or even missed targets, this paper proposes a two-layer model (initial modeloptimized model) that uses both forward and backward correlation to acquire long-term background information, thereby improving dynamic target detection performance. The method uses a regular Vibe as the initial model, whose initial background samples are initialised by a random time-based image initialisation method, and the initial background obtained from the initial model is used as an input to the optimised model. Then, based on the relationship between the initial model background and the optimised model background, freeze the background pixel update of the corresponding area of the optimised model, when there is a significant difference between the corresponding area of the static foreground in the initial model background and the optimized model background. By taking advantage of the difference between the update cycles of the front and back models, the long-term real background is obtained. Finally, a long-term background is used to detect dynamic targets and determine the target motion state. In order to verify the effectiveness and accuracy of the proposed method, this paper is validated with VISOR and SBMnet image datasets. An application example of behavioural anomaly monitoring is also given. The experimental results show that the Recall of the proposed method is significantly improved, with an average increase of 2.18%, 26.31%, 7.94%, and 6.11% compared to literature [36], Ant_Vibe, Gc_IviBe method [38], and the traditional ViBe algorithm, respectively. Compared with traditional ViBe and literature [36], the average precision is improved by 11.71% and 6.64%, respectively. Additionally, this method has better segmentation accuracy for static foreground objects, with an average FM improvement of 4.94% compared to literature [36]. From the perspectives of root mean square error (RMSE) and structural similarity (SSIM), the RMSE is reduced by 9.88% and the SSIM is improved by 1.89% compared to the background models generated by traditional ViBe in the object detection process.

Keywords:

ViBe

Target detection

Background modeling

Computer vision

Moving object detection based on ViBe long-term background modeling

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