[1]曹伟,韩华,王裕明,等.目标再确认中的优化扩散距离相似性度量[J].智能系统学报,2018,13(2):269-280.[doi:10.11992/tis.201607010]
CAO Wei,HAN Hua,WANG Yuming,et al.Target re-identification based on optimized diffusion distance[J].CAAI Transactions on Intelligent Systems,2018,13(2):269-280.[doi:10.11992/tis.201607010]
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CAO Wei,HAN Hua,WANG Yuming,et al.Target re-identification based on optimized diffusion distance[J].CAAI Transactions on Intelligent Systems,2018,13(2):269-280.[doi:10.11992/tis.201607010]
目标再确认中的优化扩散距离相似性度量
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第2期
页码:
269-280
栏目:
学术论文—人工智能基础
出版日期:
2018-04-15
- Title:
- Target re-identification based on optimized diffusion distance
- Keywords:
- optimized diffusion distance; similarity measure; multi-feature fusion; target re-identification
- 分类号:
- TP391.9
- 摘要:
- 跨摄像头目标再确认是多摄像头监控领域中一个亟需解决的难点问题，如何获得准确率更高的距离度量算法成为解决该难点的关键。为此本文在提取优秀的多特征基础上，建立了一种无需训练，适应更多场景的度量算法：优化扩散距离相似性度量，用于跨摄像头目标再确认。在高维扩散距离的基础上，加入高斯金字塔图像采样和加权性优化处理，用于提高相似空间向量的辨别力，并提高计算效率。通过对高维扩散距离的二次优化建立起最终的相似性度量函数。最后对VIPeR和ETHZ数据库中的图片进行多次目标再确认实验，排名第一的图片的正确匹配率达到了50.5%。实验结果表明本文算法取得了较好的匹配结果。
- Abstract:
- Target re-identification via cross-camera is a challenging problem in the field of multi-camera surveillance. How to get a more accurate distance measurement algorithm is the key to solve this difficult problem. So, in this paper, we established a new measurement algorithm without training based on extracting more excellent features to do target re-identification, which is optimized diffusion distance. On the basis of high-dimensional diffusion distance, Gaussian pyramid image sampling and weight optimization are added to improve the discrimination of similar space vectors and increase computational efficiency. The final similarity measure function is established by the second optimization of the high-dimensional diffusion distance. At last, we do numerous target re-identification experiments based on databases VIPeR and ETHZ. The matching rate of rank first image can reach 50.5%. The experimental results show that the algorithm proposed in this paper has good performance.
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备注/Memo
收稿日期:2016-07-13。
基金项目:国家自然科学基金项目（61305014）；国家留学基金管理委员会项目（201508310033）；上海市教育委员会和上海市教育发展基金会“晨光计划”（13CG60）；上海高校青年教师培养资助计划（ZZGJD13006）；上海工程技术大学人才行动计划（2017RC112015，nhrc-2015-11）.
作者简介:曹伟,男,1989年生,硕士研究生,主要研究方向为多目标跟踪、目标传递;韩华,女,1983年生,副教授,主要研究方向为多目标跟踪、目标再确认、目标传递、智能计算;王裕明,男,1962年生,教授,主要研究方向为计算机信息技术研究。
通讯作者:韩华.E-mail:2070967@mail.dhu.edu.cn.
更新日期/Last Update:
1900-01-01