郑州大学学报（理学版）2024，Vol.56Issue(1)：40-46,7.DOI:10.13705/j.issn.1671-6841.2022268
基于多特征融合和改进SIFT的目标跟踪算法
Target Tracking Algorithm Based on Multiple Feature Fusion and Improved SIFT
摘要
Abstract
When the moving target was deformed,rotated and interfered by the illumination or back-ground during the tracking task,it could be shifted or lost,which would reduce the tracking accuracy.A target tracking algorithm based on multiple feature fusion and improved SIFT was proposed.The feature points were extracted in the high entropy part of the image,and the mismatched feature points were elimi-nated by using the hash algorithm.At the same time,the perceptual hash and differential hash were im-proved,and the improved image hash features,color features and SIFT features were fused and applied to the tracking algorithm.The algorithm was tested on OTB-100 dataset,and the success rate reacheed 94.3%.关键词
目标跟踪/图像哈希/信息熵/颜色矩/SIFTKey words
target tracking/image hash/information entropy/color moment/SIFT分类
信息技术与安全科学引用本文复制引用
李文举,王子杰,崔柳..基于多特征融合和改进SIFT的目标跟踪算法[J].郑州大学学报（理学版）,2024,56(1):40-46,7.基金项目
国家自然科学基金项目(61973307,61903256). （61973307,61903256）
