基于局部差别性分析的目标跟踪算法

田鹏; 吕江花; 马世龙; 汪溁鹤

doi:10.11999/JEIT170045

基于局部差别性分析的目标跟踪算法

doi: 10.11999/JEIT170045

基金项目:

国家自然科学基金(61300007)

计量
- 文章访问数: 1204
- HTML全文浏览量: 95
- PDF下载量: 305
- 被引次数: 0
出版历程
- 收稿日期: 2017-01-02
- 修回日期: 2017-07-20
- 刊出日期: 2017-11-19

Robust Object Tracking Based on Local Discriminative Analysis

Funds:

The National Natural Science Foundation of China (61300007)

摘要

摘要: 在复杂场景下，为了更好地提升跟踪的鲁棒性，基于局部的相似度测量得到了广泛应用。然而，局部遮挡，形变和光照变化等场景的复杂性，基于传统局部相似度测量的目标跟踪存在很大缺点，例如，在跟踪过程中，仅仅依靠目标和模板的匹配度容易造成跟踪的偏移现象。鉴于此，该文提出一种基于局部差别性相似度测量的目标跟踪算法。首先，以目标-背景的差异性，形成相似性和差异性相结合的局部判别性相似度测量；其次，基于子块在视频序列中的差异性，对子块进行差异性学习，以提高跟踪的准确性。最后，在粒子滤波框架下，基于差别性局部区域测量构建了一种有效的目标跟踪算法。实验结果表明，在复杂图像序列中，该算法实现了目标的准确跟踪，并在光照变化、旋转、缩放和遮挡等方面具有较好的效果。
- 目标跟踪 /
- 局部稀疏表示 /
- 相似度测量 /
- 判别性分析 /
- 差异性权重
Abstract: The local similarity measurements are usually used for improving the tracking robustness under the complex scene. However, this method have drawbacks in cases of partial occlusion, deformation and rotation. For example, the method only considers traditional similarity measurements of targets and templates, results in the matching errors to lead to tracking failure. In this paper, a target tracking algorithm is proposed based on measurements of the local difference similarities. The presented method has the following advantages: firstly, both similarities and differences are considered for measurement; secondly, the differential weight learning of the local region is carried out to improve the accuracy of sub-block difference measurement; at last, an effective and efficient tracker is designed based on the difference analysis and a simple update manner within the particle filter framework. Experimental results show that the proposed algorithm achieves better performance than traditional competing methods in various factors, such as illumination changes, part occlusion, scale changes and so on.
- Object tracking /
- Local sparse representation /
- Similarity measurement /
- Discriminative analyses /
- Difference weight

HTML全文

参考文献(25)

刘红亮, 周生华, 刘宏伟, 等. 一种航迹恒虚警的目标检测跟踪一体化算法[J]. 电子与信息学报, 2016, 38(5): 1072-1078. doi: 10.11999/JEIT150638.

LIU Hongliang, ZHOU Shenghua, LIU Hongwei, et al. An integrated target detection and tracking algorithm with constant track false alarm rate[J]. Journal of Electronics Information Technology, 2016, 38(5): 1072-1078. doi: 10.11999/JEIT150638.

MARKUS Z, THOMAS N, and ANDREAS K. Tracking human locomotion by relative positional feet tracking[C]. 2015 IEEE Virtual Reality (VR), Arles, France, 2015: 317-318.

EVANGELO S, HATICE G, and ANDREA C. Automatic analysis of facial affect: A survey of registration, representation, and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(6): 1113-1133.

SAI Y, BO X, Li P Y, et al. Robust scene matching method based on sparse representation and iterative correction[J]. Image and Vision Computing, 2017, 60(4): 115-123.

PENG P L, ERIK B, and HAI B L. Encoding color information for visual tracking: algorithms and benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5630-5644. doi: 10.1109/TIP.2015.2482905.

BABENKO B, YANG M H, and BELONG S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632. doi: 10.1109/TPAMI.2010.226.

ZHANG K, ZHANG L, and YANG M H. Real-time object tracking via online discriminative feature selection[J]. IEEE Transactions on Image Processing, 2013, 22(12): 4664-4677. doi: 10.1109/TIP.2013.2277800.

LAURA S L and ERIK L M. Distribution fields for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 1910-1917.

BAO C, WU Y, LING H, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, 2012: 1830-1837.

MEI X, LING H, and WU Y. Efficient minimum error bounded particle resampling LI tracker with occlusion detection[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2661-2675.

KWON J and LEE K M. Highly non-rigid object tracking via patch-based dynamic appearance modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2427-2441.

LIU B, HUANG J, YANG L, et al. Robust tracking using local sparse appearance model and k-selection[C]. IEEE Conference on Computer Vision and Pattern Recognition. Colorado, CO, USA, 2011, 201: 1313-1320.

JIA X, LU H, and YANG M H. Visual tracking via adaptive structural local sparse appearance model[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), USA, 2012: 1822-1829.

ADAM A, RIVLINi E, and SHIMSHONI I. Robust fragment- based tracking using the integral histogram[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), USA, 2006: 798-805.

HE S, YANG Q, and Yang M H. Visual tracking via locality sensitive histograms[C]. IEEE Conference Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, 2013: 2427-2434.

JIA Y M, HUA B, JI Z, et al. Robust feature matching for remote sensing image registration via locally linear transforming[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6469-6481. doi: 10.1109/ TGRS.2015.2441954.

WANG D, LU H C, and BO C J. Visual tracking via weighted local cosine similarity[J]. IEEE Transactions on Cybernetics, 2015, 45(9): 1838-1850. doi: 10.1109/TCYB. 2014.2360924.

LIU H C, LI S T, and FANG L Y. Robust object tracking based on principal component analysis and local parse representation[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(11): 2863-2875. doi: 10.1109/TIM. 2015.2437636.

ZHUANG B H, LU H C, XIAN Z Y, et al. Visual tracking via discriminative sparse similarity map[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1872-1881. doi: 10.1109/ TIP.2014.2308414.

毕笃彦, 库涛, 查宇飞, 等. 基于颜色属性直方图的尺度目标跟踪算法研究[J]. 电子与信息学报, 2016, 38(5): 1009-1106. doi: 10.11999/JEIT150921.

BI Duyan, KU Tao, ZHA Yufei, et al. Scale-adaptive object tracking based on color names histogram[J]. Journal of Electronics Information Technology, 2016, 38(5): 1009-1106. doi: 10.11999/JEIT150921.

占荣辉, 刘盛启, 欧建平, 等. 基于序贯蒙特卡罗概率假设密度滤波的多目标检测前跟踪改进算法[J]. 电子与信息学报, 2014, 36(11): 2593-2599. doi: 10.3724/SP.J.1146.2013.02029.

ZHAN Ronghui, LIU Shengqi, OU Jianping , et al. Improved multi target track before detect algorithm using the sequential monte carlo probability hypothesis density filter[J]. Journal of Electronics Information Technology, 2014, 36(11): 2593-2599. doi: 10.3724/SP.J.1146.2013.02029.

ORONR S and AHARON B H. Locally orderless tracking[J]. International Journal of Computer Vision, 2015, 111(2): 213-228. doi: 10.1109/CVPR.2012.6247895.

施引文献

资源附件(0)

访问统计