Background subtraction algorithm is a kind of main moving object detection framework,but it is too diffi-cult to build a model with short establishing period,high reliability and good robustness.From the perspective of scene per-ception,a technique for object detection based on the framework of background subtraction is proposed.To improve the relia-bility of initial model,the potential foreground pixels in background,which are obtained on the basis of stable structural in-formation in the former two frames,are replaced by the nearest neighbor pixels belonging to background,when the initial model is being established in the second frame.Integrating color information with binary feature,a two-stage classification decision mechanism is proposed,meanwhile the local decision threshold and update frequency are adaptively adjusted in ac-cordance with the texture complexity of pixels neighborhood.Subsequently,a feedback mechanism for misclassification is presented in the update model phase.Experimental results using challenging public video sequences show the effectiveness and superiority of the proposed method,compared with other state-of-the-art tracking approaches.%背景减除法是一种主要的运动目标检测框架，但在复杂环境中构建一种初始模型建立周期短、可靠性高、鲁棒性好的模型仍是一大难题。本文从场景感知的角度出发，在背景减除框架的基础上提出一种目标检测方法。该方法根据前两帧中稳定的结构信息感知背景中潜在的前景区域，在第二帧建立初始模型时利用最近邻域背景像素点代替可能的前景像素点，提高了初始模型可靠性；结合颜色信息和二进制特征提出了像素点二级分类判决机制，并通过感知像素点邻域内的纹理复杂度自适应调整局部判决阈值和更新频率；在模型更新阶段提出处理误判的反馈机制。在公开视频序列上同几种流行检测算法的实验对比结果证明了本文算法的有效性和优越性。
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