Background Modeling Algorithm for Multi-feature Fusion
Zhicheng Guo, Jianwu Dang, Yangping Wang, Jing Jin · 2019
In order to improve the accuracy of foreground target detection and establish a stable background model, this paper proposes a multi-feature fusion background modeling algorithm, which initializes the background model with the spatial correlation between the first frame pixel and the domain pixel, and quickly establishes the background. model. A multi-feature sample set consisting of pixel values, update frequency, update time, and adaptive dynamic coefficients is updated with temporal correlation of subsequent intra-pixels. According to the multi-feature sample set, the background complexity is adjusted to adjust the update speed of the model in different regions, which effectively improves the ghost phenomenon of the foreground target and reduces the false holes in the target and the false foreground in the background. The test results of multiple sets of data sets show that the proposed algorithm improves the adaptability and robustness of foreground target detection in scenarios with high dynamic changes.