基于自适应分块和SSIM的运动目标检测

Moving Object Detection Based on Adaptive Image Blocking and SSIM

研究了目标检测方法。针对传统背景更新方法易受噪声干扰、算法执行速度慢等弊端,对背景差分法予以改进,提出一种基于自适应图像分块和结构相似性(SSIM)的运动目标检测方法。根据视频最初几帧得到初始背景模型,再对视频后续的每帧进行自适应分块处理,利用相邻帧对应分块的结构相似性计算局部更新率,建立背景模型,将背景与当前帧差分即得到运动目标。实验结果表明,与传统的背景差分法相比,改进后的方法具有更好的检测效果。

This paper focused on object detection. Motivated by the drawbacks of existing background update algorithms that are noise sensitive and slow in execution, an improvement on moving obiect detection method was proposed by image adaptive blocking and block-wise structure similarity of inter-frames. An initial background model was obtained with a few beginning frarnes and every successive frame was divided into blocks. Over corresponding blocks of two neighbor- ing frames, a similarity was defined in order to update the background model. The moving objects were then obtained by subtracting the background model from the current frame. Experimental results demonstrate that the improved method has better performance than traditional methods.