摘要
为解决传统尺度不变特征变换(SIFT)算法在光照变化和遮挡的情况下,不能快速准确跟踪目标的问题,提出一种采用粒子滤波和SIFT建立目标模型的方法,利用粒子滤波预测目标在当前帧中可能的位置。计算目标可能存在的区域SIFT特征点,构建特征描述向量,进行目标匹配。根据目标模型和目标候选区域中SIFT特征点的匹配情况,在跟踪过程中更新特征描述向量,实现目标跟踪。实验结果证明,该算法可提高目标检测和跟踪的速度以及准确性。
In the case of illumination varies and shelter,the traditional algorithm can not track the targets fast and precisely.In order to solve the problem,a new method is proposed for target tracking,in which particle filtering is used to establish the target motion model and Scale Invariable Feature Transformation（SIFT） feature is introduced to create the target model.The possible position of the target in the current frame is predicted by particle filtering.The SIFT feature points of the possible area are calculated and the characteristics description vector is built to match the target.According to the matching result of the target model and the SIFT feature points of the target candidate region,the characterization vector is updated in the tracking process.Experimental results show that the proposed algorithm can improve the speed and accuracy of the target detection and tracking significantly.
出处
《计算机工程》
CAS
CSCD
2012年第10期14-17,共4页
Computer Engineering
关键词
尺度不变特征变换
粒子滤波
模型更新
目标跟踪
搜索策略
特征描述向量
Scale Invariable Feature Transformation（SIFT）
particle filtering
model update
object tracking
search strategy
characterizationvector
