Research And Application Of Video Foreground-Background Separation Algorithm Based On Non-convex And Non-smooth Optimization

Machine vision,as one of the three fields of artificial intelligence research,has attracted extensive attention.Video foreground-background separation,as a major challenge in machine vision,has become one of the core problems which need to be solved imperatively in machine vision.The excellent performance of low-rank and sparse model makes it become a hot spot in the field of machine vision in recent years.To tackle the problem of video foreground-background separation,this paper has completed mainly the following work:Firstly,a new video foreground-background separation model is established based on weighted Schatten-p norm and structured sparse norm,and a algorithm of low-rank and sparse decomposition for video foreground-background separation is proposed based on the model.This algorithm can approximate the rank function more accurately,suppress the noise generated during the measurement,and judge the foreground target more accurately by using the structured informations of foreground,which is more beneficial to the foreground modeling.The simulation results show that the proposed algorithm is not robust to the scene of sudden illumination,but it can obtain a good separation effect in the static background and better separation effect in the complex and varied dynamic background.Secondly,a new video foreground-background separation model is established based on truncated nuclear norm and structured sparse norm,and a algorithm of low-rank and sparse decomposition for video foreground-background separation is proposed.By using the spatial structured information of foreground,we can judge the foreground target more accurately,which is more beneficial to the modeling of foreground.In nine groups of video scenes with different features,the simulation results show that the proposed algorithm is not robust to some scenes such as the light sudden change,but it can achieve a good separation effect in the static background,and better separation effect in the complex and diverse dynamic background.To sum up,for the problem of video foreground-background separation,the proposed algorithms in this paper perform better than the existing mainstream algorithms,which verify the feasibility of the algorithms and lay a foundation for the practical application.