激光与光电子学进展, 2011, 48 (7): 071001, 网络出版: 2011-06-02
运动序列中动目标检测的稳健性方法
Robust Method for Moving Object Detection in Dynamic Background
动目标检测 尺度不变特征 对称性约束 随机抽样一致集算法 运动估计 moving object detection scale invariant feature transform symmetry constraint random sample consensus motion estimation
摘要
提出一种运动序列中动目标检测的稳健性方法。用尺度不变特征变换(SIFT)算法生成特征描述符,基于最近邻距离比(NNDR)进行初始匹配,增加对称性约束以获得稳健的匹配点集。随机抽样一致集算法(RANSAC)用于分离背景和目标对应特征点,实现背景运动的稳健性估计。背景补偿后,相邻帧差分和数学形态学方法实现动目标的分割。真实运动序列的实验结果表明,该算法能够获得稳健的匹配点对,检测出运动目标。
Abstract
A robust approach to detect moving object in dynamic background is proposed. The feature descriptions are generated by scale invariant feature transform (SIFT) algorithm. Initial match set is obtained according to nearest neighbor distance ratio(NNDR) strategy, and a robust match set is obtained by using the constraint of symmetry. Random sample consensus(RANSAC) algorithm is applied to distinguish features in moving object region from the background. Based on background compensation, the moving object region is segmented by inter-frame difference and morphology operations. Experimental results on real video sequences from moving cameras demonstrate that the proposed algorithm can obtain a set of robust feature correspondences and detect moving object.
喻夏琼, 陈向宁. 运动序列中动目标检测的稳健性方法[J]. 激光与光电子学进展, 2011, 48(7): 071001. Yu Xiaqiong, Chen Xiangning. Robust Method for Moving Object Detection in Dynamic Background[J]. Laser & Optoelectronics Progress, 2011, 48(7): 071001.