基于谱残差和聚类法的运动目标检测研究

传统基于特征点匹配的目标检测算法目标识别率低、误检率较高是因为特征点匹配不准确、目标轮廓不连续。针对这一问题，分别引入谱残差算法和k means聚类算法，并加以改进，提出一种基于谱残差算法和k means聚类算法的运动目标检测算法。具体方法是：首先，每隔两帧提取加速鲁棒特征SURF并对图像配准，再对帧差结果采用谱残差算法提取视觉显著性特征，去除因匹配不准确造成的噪点和伪运动目标；其次，形态学处理之后引入改进后的k means聚类算法，对不连续的轮廓进行聚类；最后形成完整的目标。实验显示，本文算法目标识别率达到90.61%，误检率达到21.25%,分别优于传统基于SURF特征的运动目标检测算法66.60%的识别率、31.91%的误检率和基于新的局部不变性特征ORB匹配的目标检测算法87.573%的识别率、26.80%的误检率。虽然该算法平均运行时间为18 fps，但仍可以满足视频流畅的需求，因此动态背景下该算法可做为一种有效的运动目标检测算法使用。

A moving object detection algorithm based on spectral residual algorithm and clustering algorithm

The traditional target detection algorithm based on feature point matching has low target recognition rate and high false detection rate because of inaccurate matching of feature points and target contour discontinuity. We introduce the improved spectral residual algorithm and k-means clustering algorithm to solve those problems, and propose a moving target detection algorithm based on spectral residual algorithm and clustering algorithm. The method is divided into two parts. Firstly, we extract the speed up robust features (SURF) from every two frames and complete image registration. Then, we use the spectral subtraction algorithm to extract the saliency feature from frame difference results in order to remove noise and false targets caused by inaccurate matching. Secondly, the improved k-means clustering algorithm is introduced to cluster the discontinuous contour curves to obtain a complete target after morphological processing. Experiments show that the target recognition rate of this new algorithm is 90.61% and the false detection rate is 21.23%, which are better than the traditional moving object detection algorithm based on SURF whose corresponding results are 66.60% and 31.91% respectively and the moving object detection algorithm based on improved oriented FAST and rotated BRIEF (ORB) whose corresponding results are 87.57% and 26.80% respectively. Although the average running time of the algorithm is 18 frames/s, it can meet the requirement of video fluency. This algorithm therefore can be used as an effective moving target detection algorithm in dynamic background.