基于部件融合特征的车辆重识别算法
Vehicle Re-identification Algorithm Based on Component Fusion Feature
查看参考文献15篇
|
文摘
|
针对车辆型号相同但车辆个体不同的重识别问题,提出一种新的车辆重识别算法。运用部件检测算法获取不同车辆之间差异较大的车窗和车脸区域,对检测到的车窗和车脸区域进行特征提取并进行融合,生成新的融合特征,计算图像特征之间距离度量进行分类识别。在中山大学公开数据集VRID-1上进行测试,结果表明,该算法的Rank1匹配率达到66.67%,明显优于经典的传统特征表征算法,从而验证该算法是可行且有效的。 |
|
其他语种文摘
|
To address the re-identification problem of different individual vehicles with identical types,a new vehicle reidentification algorithm is proposed.According to the component detection algorithm,the window and the vehicle face region with large differences between different vehicles are obtained,and the vehicle features of the detected vehicle window and the vehicle face region are extracted and merged to generate new fusion features.The distance measurement between image features is calculated for classification and recognition.The test is carried out on the public dataset VRID-1 of Sun Yat-sen university and results show that the Rank1 matching rate of the algorithm reaches 66.67%,which is obviously better than the classical traditional feature representation algorithm,thus verifies the feasibility and validity of the algorithm. |
|
来源
|
计算机工程
,2019,45(6):12-20 【扩展库】
|
|
DOI
|
10.19678/j.issn.1000-3428.0052284
|
|
关键词
|
车辆重识别
;
部件检测
;
特征提取
;
特征融合
;
距离度量
|
|
地址
|
1.
中山大学智能工程学院, 广州, 510006
2.
广东省智能交通系统重点实验室, 广东省智能交通系统重点实验室, 广州, 510006
3.
视频图像智能分析与应用技术公安部重点实验室, 视频图像智能分析与应用技术公安部重点实验室, 广州, 510006
4.
视频图像信息智能分析与共享应用技术国家工程实验室, 视频图像信息智能分析与共享应用技术国家工程实验室, 北京, 100048
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-3428 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
|
|
文献收藏号
|
CSCD:6513196
|
参考文献 共
15
共1页
|
|
1.
Mei L T. Application of automatic vehicle identification technology for real-time journey time estimation.
Information Fusion,2011,12(1):11-19
|
被引
1
次
|
|
|
|
|
2.
张耿宁. 基于特征融合的行人重识别方法.
计算机工程与应用,2017,53(12):185-189
|
被引
6
次
|
|
|
|
|
3.
刘娜.
基于卷积神经网络的行人重识别算法,2017
|
被引
2
次
|
|
|
|
|
4.
Cheng Deng. Person re-identification by multi-channel parts-based CNN with improved triplet loss function.
Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:1335-1344
|
被引
7
次
|
|
|
|
|
5.
Tian Yin. A vehicle reidentification algorithm based on multi-sensor correlation.
Journal of Zhejiang University-SCIENCE C (Computers and Electronics),2014,15(5):372-382
|
被引
1
次
|
|
|
|
|
6.
Coifman B. Vehicle reidentification and travel time measurement,part II: uncongested freeways and the onset of congestion.
Proceedings of 2001 IEEE Intelligent Transportation Systems,1999:899-917
|
被引
1
次
|
|
|
|
|
7.
王盼盼. 基于特征融合和L-M算法的车辆重识别方法.
电子科技,2018,4(1):12-15
|
被引
1
次
|
|
|
|
|
8.
Zhang Yiheng. Improving triplet-wise training of convolutional neural network for vehicle re-identification.
Proceedings of IEEE International Conference on Multimedia and Expo,2017:1386-1391
|
被引
2
次
|
|
|
|
|
9.
Xu Qingtong. Learning a repression network for precise vehicle search.
Proceedings of IEEE CVPR'17,2017:125-136
|
被引
1
次
|
|
|
|
|
10.
Zapletal D. Vehicle re-identification for automatic video traffic surveillance.
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops,2016:1568-1574
|
被引
1
次
|
|
|
|
|
11.
Zhou Yi. Vehicle re-identification by deep hidden multi-view inference.
IEEE Transactions on Image Processing,2018,27(7):3275-3287
|
被引
8
次
|
|
|
|
|
12.
Ren Shaoqing. Faster R-CNN: towards realtime object detection with region proposal networks.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149
|
被引
1701
次
|
|
|
|
|
13.
Szegedy C. Going deeper with convolutions.
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2015:1-9
|
被引
90
次
|
|
|
|
|
14.
Macqueen J. Some methods for classification and analysis of multi-variate observations.
Proceedings of Berkeley Symposium on Mathematical Statistics and Probability,1967:281-297
|
被引
8
次
|
|
|
|
|
15.
Li Xiying. VRID-1: a basic vehicle re-identification dataset for similar vehicles.
Proceedings of IEEE International Conference on Intelligent Transportation Systems,2017:1-8
|
被引
2
次
|
|
|
|
|
了解气象卫星更多信息，请访问