关键词: ViBe算法, 运动目标检测, 自适应参数, 鬼影抑制

Abstract: In order to solve the problems of fixed parameter setting and ghost, an adaptive strategy for the ViBe(Visual Background Extractor) motion object detection algorithm is proposed, which includes three improved adaptive methods. First, for the sample selection, it expands the sample neighborhood with the uniform random number, which can avoid to select pixels repeatedly and reduce pixel error classification. Then for fixed match radius and background updator in ViBe, adaptive strategies are used respectively that matching radius are set by the degree of dynamic background and background updator is dynamically regulated by the motion velocity. Finally the optimal threshold of quadratic discriminant obtained by an iterative method is used to filter out the misjudgment area and eliminate the ghost areas. Compared with frame difference method, GMM(Gaussian Mixture Model), CodeBook, LSD(Low-rank and Sparse Decomposition), DECOLOR (Detecting Contiguous Outliers in the Low-rank Representation), and ViBe algorithm on the two public video datasets, Change Detection and LASIESTA, the proposed method has a better performance and it is robust for moving object detection in complex background.

Key words: ViBe algorithm, moving object detection, adaptive parameters, ghost suppression