针对复杂场景下的背景建模，提出了一种基于函数估计的自适应模糊方法．该方法使用 TSK 模糊系统作为估计算子，分别使用粒子群优化( PSO) 算法和递归最小二乘估计( RLSE) 算法来优化模糊系统的前件参数和后件参数．为有效地估计背景，文中将前景像素看作背景像素的异常样例，提出了去除异常样例的方法， 然后用去除异常样例后的结果去训练模糊估计算子．在多段不同视频序列上的实验结果表明，该方法在动态背景、光照变化、摄像机振动等环境下均具有较高的运行效率和检测效果．

Aiming at the background modeling in complex scenes,this paper proposes a novel adaptive fuzzy method based on function estimation.In this method,the Takagi- Sugeno- Kang (TSK) fuzzy system is taken as the estimator,and the parameters of the premise part and the consequent part of the fuzzy system are optimized by combining the particle swarm optimization (PSO) with the recursive least squares estimator (RLSE).In order to effectively esti-mate the background,the foreground samples are interpreted as outliers relative to the background samples,and an outlier separator method is devised.After the outliers are removed,the obtained results are used to train the fuzzy estimator.Finally,through the experiments of different video sequences,it is found that the proposed method is accurate and effective in such enviromnets as dynamic background,illumination changes and camera vibration.