基于随机一致性采样估计的目标跟踪算法

doi:10.11772/j.issn.1001-9081.2016.09.2566

计算机应用 ›› 2016, Vol. 36 ›› Issue (9): 2566-2569.DOI: 10.11772/j.issn.1001-9081.2016.09.2566

基于随机一致性采样估计的目标跟踪算法

勾承甫^1,2, 陈斌^1,2, 赵雪专^1,2, 陈刚^1,2

1. 中国科学院成都计算机应用研究所, 成都 610041;
2. 中国科学院大学, 北京 100049

收稿日期:2016-01-29 修回日期:2016-03-04 出版日期:2016-09-10 发布日期:2016-09-08
通讯作者: 勾承甫
作者简介:勾承甫(1989-),男,四川绵阳人,硕士研究生,要研究方向:图像处理、计算机视觉;陈斌(1970-),男,四川广汉人,研究员,博士生导师,主要研究方向:图像分析、机器视觉;赵雪专(1986-),男,河南濮阳人,博士研究生,主要研究方向:图像分析、机器视觉;陈刚(1984-),男,四川乐山人,博士研究生,主要研究方向:图像分析、机器学习。
基金资助:
四川省科技成果转换项目（2014CC0043）。

Object tracking algorithm based on random sampling consensus estimation

GOU Chengfu^1,2, CHEN Bin^1,2, ZHAO Xuezhuan^1,2, CHEN Gang^1,2

1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China;
2. University of Chinese Academy Sciences, Beijing 100049, China

Received:2016-01-29 Revised:2016-03-04 Online:2016-09-10 Published:2016-09-08
Supported by:
This work is partially supported by the Science and Technology Achievement Transformation Foundation of Sichuan Province (2014CC0043).

摘要/Abstract

摘要： 为了解决在实际监控中因为目标遮挡、外观变化和时间过长导致跟踪丢失的问题，提出一种基于随机一致性采样（RANSAC）估计的目标跟踪算法。算法首先在搜索区域提取局部不变特征集，然后利用特征匹配传递性和非参数学习算法从特征集中分离出目标特征，最后对目标特征进行RANSAC估计跟踪目标位置。将算法在不同场景的视频数据集上进行测试，分别从准确率、召回率和综合评价指标F1-Measure三个指标分析算法性能，实验结果表明所提出的算法提高了目标跟踪的准确性，克服了长时间目标跟踪产生的跟踪漂移。

关键词: 局部不变特征, 匹配传递性, 非参数学习, 随机一致性采样估计, 目标跟踪

Abstract: In order to solve tracking failure problem caused by target occlusion, appearance variation and long time tracking in practical monitoring, an object tracking algorithm based on RANdom SAmpling Consensus (RANSAC) estimation was proposed. Firstly, the local invariant feature set in the searching area was extracted. Then the object features were separated from the feature set by using the transfer property of feature matching and non-parametric learning algorithm. At last, the RANSAC estimation of object features was used to track the object location. The algorithm was tested on video data sets with different scenarios and analyzed by using three analysis indicators including accuracy, recall and comprehensive evaluation (F1-Measure). The experimental results show that the proposed method improves target tracking accuracy and overcomes track-drift caused by long time tracking.

Key words: local feature invariance, transitive matching property, non-parametric learning, RANdom SAmpling Consistency (RANSAC) estimation, object tracking

中图分类号:

TP391.4

勾承甫, 陈斌, 赵雪专, 陈刚. 基于随机一致性采样估计的目标跟踪算法[J]. 计算机应用, 2016, 36(9): 2566-2569.

GOU Chengfu, CHEN Bin, ZHAO Xuezhuan, CHEN Gang. Object tracking algorithm based on random sampling consensus estimation[J]. Journal of Computer Applications, 2016, 36(9): 2566-2569.

参考文献

[1] 董慧芬,董保磊,丁小芬,等.基于相关区域分层的改进Meanshift目标跟踪算法 [J].计算机应用,2014,34(S2):286-290.(DONG H F, DONG B L, DING X F, et al. Improved Meanshift tracking algorithm based on relevant regional stratification [J]. Journal of Computer Applications, 2014, 34(S2): 286-290.)
[2] 习文星,汤心溢.基于随机森林和支持向量机的快速行人检测算法[J].计算机应用,2014,34(S2):283-285.(XI W X, TANG X Y. Pedestrian detection based on random forest and support vector machine [J]. Journal of Computer Applications, 2014, 34(S2): 283-285.)
[3] 付忠良,赵向辉,苗青,等.基于属性组合的集成学习算法[J].计算机应用,2010,30(2):465-468.(FU Z L, ZHAO X H, MIAO Q, et al. Ensemble learning algorithm on attribute combination [J]. Journal of Computer Applications, 2010, 30(2): 465-468.)
[4] BONIN-FONT F, ORTIZ A, OLIVER G. Visual navigation for mobile robots: a survey [J]. Journal of Intelligent and Robotic Systems, 2008, 53(3): 263-296.
[5] HU W, XIE D, FU Z, et al. Semantic-based surveillance video retrieval [J]. IEEE Transactions on Image Processing, 2007, 16(4): 1168-1181.
[6] REMONDINO F. 3-D reconstruction of static human body shape from image sequence [J]. Computer Vision and Image Understanding, 2004, 93(1): 65-85.
[7] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[8] WANG S, LU H, YANG F, et al. Superpixel tracking [C]// Proceedings of the 2011 IEEE International Conference on Computer Vision. Piscataway, NJ: IEEE, 2011:1323-1330.
[9] DINH T B, VO N, MEDIONI G. Context tracker: exploring supporters and distracters in unconstrained environments [C]// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2011:1177-1184.
[10] BABENKO B, YANG M H, BELONGIE S. Robust object tracking with online multiple instance learning [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
[11] SANTNER J, LEISTNER C, SAFFARI A, et al. PROST: parallel robust online simple tracking [C]// Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2010:723-730.
[12] YU Q, DINH T B, MEDIONI G. Online tracking and reacquisition using co-trained generative and discriminative trackers [C]// Proceedings of the 10th European Conference on Computer Vision. Berlin: Springer, 2008: 678-691.
[13] AVIDAN S. Ensemble tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 261-271.
[14] LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[15] GRABNER H, BISCHOF H. On-line boosting and vision [C]// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2006: 260-267.
[16] GRABNER H, LEISTNER C, BISCHOF H. Semi-supervised on-line boosting for robust tracking [C]// ECCV '08: Proceedings of the 10th European Conference on Computer Vision. Berlin: Springer, 2008: 234-247.
[17] KALAL Z, MATAS J, MIKOLAJCZYK K. P-N learning: boot-strapping binary classifiers by structural constraints [C]// Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2010: 49-56.
[18] PERNICI F, DEL BIMBO A. Object tracking by oversampling local features [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(12): 2538-2551.

基于随机一致性采样估计的目标跟踪算法

Object tracking algorithm based on random sampling consensus estimation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	姬张建, 任兴旺. 带旋转与尺度估计的全卷积孪生网络目标跟踪算法[J]. 计算机应用, 2021, 41(9): 2705-2711.
[2]	钟莎, 黄玉清. 基于孪生区域候选网络的无人机指定目标跟踪[J]. 计算机应用, 2021, 41(2): 523-529.
[3]	张杰, 常天庆, 戴文君, 郭理彬, 张雷. 基于相关滤波与颜色概率模型的目标跟踪算法[J]. 计算机应用, 2020, 40(6): 1774-1782.
[4]	何祥宇, 李静, 杨数强, 夏玉杰. 基于ET-PHD滤波器和变分贝叶斯近似的扩展目标跟踪算法[J]. 计算机应用, 2020, 40(12): 3701-3706.
[5]	石国强, 赵霞. 基于联合优化的强耦合孪生区域推荐网络的目标跟踪算法[J]. 计算机应用, 2020, 40(10): 2822-2830.
[6]	杨博, 蔺素珍, 禄晓飞, 李大威, 秦品乐, 左健宏. 基于多特征融合与分层数据关联的空中红外多目标跟踪方法[J]. 计算机应用, 2020, 40(10): 3075-3080.
[7]	何祥宇, 于斌, 夏玉杰. 粒子概率假设密度平滑器异常平滑问题的解决方法[J]. 计算机应用, 2020, 40(1): 299-303.
[8]	俞皓芳, 孙力帆, 付主木. 基于改进K-means++聚类的多扩展目标跟踪算法[J]. 计算机应用, 2020, 40(1): 271-277.
[9]	贾永超, 何小卫, 郑忠龙. 融合重检测机制的卷积回归网络目标跟踪算法[J]. 计算机应用, 2019, 39(8): 2247-2251.
[10]	韩玉兰, 韩崇昭. 非线性系统的多扩展目标跟踪算法[J]. 计算机应用, 2019, 39(5): 1318-1324.
[11]	杨春德, 刘京, 瞿中. 分离窗口快速尺度自适应目标跟踪算法[J]. 计算机应用, 2019, 39(4): 1145-1149.
[12]	胡秀华, 王长元, 肖锋, 王亚文. 利用空间结构信息的相关滤波目标跟踪算法[J]. 计算机应用, 2019, 39(4): 1150-1156.
[13]	黄帅, 付光远, 伍明, 岳敏. 未知环境基于单目次优视差的多模滤波目标跟踪算法[J]. 计算机应用, 2019, 39(3): 864-868.
[14]	熊晓璇, 王文伟. 基于快速判别式尺度估计的核相关滤波方法[J]. 计算机应用, 2019, 39(2): 546-550.
[15]	闫若怡, 熊丹, 于清华, 肖军浩, 卢惠民. 基于并行跟踪检测框架与深度学习的目标跟踪算法[J]. 计算机应用, 2019, 39(2): 343-347.