摘要
针对传统车辆检索方法中存在准确性和区分度较低的问题,提出了一个基于改进SURF(speeded up robust features)算法的视频车辆检索方法。在车辆视频关键帧提取的基础上,根据改进SURF算法完成车辆图像的特征提取及匹配,其中包含改进FAST(features from accelerated segment test)特征点检测、SURF特征向量提取以及最近邻查询方法来进行特征点的匹配;通过计算比较待检索车辆图像与数据库车辆图像的相似度,算法完成图像筛选并反馈检索结果。实验结果表明:针对交通监控视频中待检索车辆,该方法能够较为准确地进行检索并反馈结果。
To overcome the problem low accuracy and discrimination of traditional vehicle retrieval methods, a new vehicle video retrieval method based on improved SURF algorithm is proposed. On the basis of vehicle video key frame extraction, the improved SURF algorithm is used for extracting and matching of vehicle image features, inclu- ding improved FAST angular point algorithm to extract the image feature points, including SURF algorithm to extract the image feature vector and including nearest neighbor query algorithm to get matching points; through cal- culating and comparing vehicle images to be retrieved and the database of vehicle images in similarity, image filte- ring is completed and the retrieval results are output. The experimental results and their analysis show preliminarily that this method not only can detect the video vehicle, but also can feedback retrieval results fairly accurately.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2014年第2期297-302,共6页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61201321)
西北工业大学研究生创业种子基金(Z2014153)资助
关键词
车辆视频检索
改进SURF算法
改进FAST特征点
特征点匹配
相似度
algorithms, automobiles, calculations, computer programming languages, experiments, feature extrac-tion, flowcharting, image matching, image retrieval
improved SURF（Speeded Up Robust Features）algorithm, improved FAST key points, key points matching, similarity, vehicle retrieval
