Journal Article

FreeViBe+: An Enhanced Method for Moving Target Separation

Lin Jiaxiang, Zhang Keju, Wu, Jianwei, Shen Yuhan

2025

Overview

An enhanced method called FreeViBe+ for moving target segmentation is proposed in this paper, addressing limitations in the ViBe algorithm such as ghosting, shadows, and holes. To eliminate ghosts, multi-frame background modeling is introduced. Shadows are detected and removed based on their characteristics in the HSV color space, while holes are filled by merging GrabCut segmentation results with the ViBe extraction output. Furthermore, the Structure-measure is tuned to optimize image fusion, enabling improved foreground–background separation. Comprehensive experiments on the UCF101 and Weizmann datasets demonstrate the effectiveness of FreeViBe+ in comparison with Finite Difference, Gaussian Mixture Model, and ViBe methods. Ablation studies confirm the individual contributions of multi-frame modeling, shadow removal, and GrabCut refinement, while sensitivity analysis verifies the robustness of key parameters. Quantitative evaluations show that FreeViBe+ achieves superior performance in precision, recall, and F-measure compared with existing approaches.

• Publisher: MDPI AG
• Subject: Ablation / Accuracy / Algorithms / Computer vision / Deep learning / Efficiency / GrabCut / Moving targets / Neural networks / Parameter sensitivity / Principal components analysis / Probabilistic models / Sensitivity analysis / Separation / Shadows / structure-measure / target separation / ViBe