基于核相关滤波与特征融合的分块跟踪算法

doi:10.16088/j.issn.1001-6600.2020.05.002

Abstract

Abstract: In order to make the tracker based kernel correlation filter to overcome occlusion and deformation, an adaptive block tracking algorithm is proposed by using kernel correlation filtering and multi-feature fusion. Firstly, adaptively divide it into blocks based on the size and height-width ratios of the target and extract the Histogram of oriented gradient (HOG) and color name (CN) features for each block; Secondly, represent the block with the fusion of HOG and CN, and use the kernel correlation filtering tracker to get the position with maximum response of each block; Thirdly, calculate the coordinate of candidate target through the coordinate geometric relationship between each block and the original target; Moreover, the final target position is obtained by weighted average of the coordinates of all coordinates. Finally, the peak to side lobe ratio(PSR) of a blocks response curve as well as the distance between its response position and the final target position are used to judge its validity, and the valid blocks are adaptively updated. The experimental results on several data set show that the proposed method performs well on occlusion and deformation, and its comprehensive performance is better than other methods.

Key words: target tracking, correlation filtering, target block, feature fusion, model update

CLC Number:

TP391.41

ZHANG Canlong, LI Yanru, LI Zhixin, WANG Zhiwen. Block Target Tracking Based on Kernel Correlation Filter and Feature Fusion[J].Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(5): 12-23.

References

[1] OJHA S, SAKHARE S. Image processing techniques for object tracking in video surveillance:a survey[C] // 2015 International Conference on Pervasive Computing (ICPC). Piscataway, NJ:IEEE Press, 2015:302-309. DOI: 10.1109/PERVASIVE.2015.7087180.
[2] 黄凯奇, 陈晓棠, 康运锋, 等. 智能视频监控技术综述[J]. 计算机学报, 2015, 38(6): 1093-1118. DOI: 10.11897/SP.J.1016.2015.01093.
[3] MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking[C] // Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I.Berlin: Springer, 2016: 445-461. DOI: 10.1007/978-3-319-46448-0_27.
[4] BOULAHIA S Y, ANQUETIL E, MULTON F, et al. Dynamic hand gesture recognition based on 3D pattern assembled trajectories[C] // 2017 Seventh International Conference on Image Processing Theory, Tools and Applications. Piscataway, NJ: IEEE Press, 2017: 1-6. DOI: 10.1109/IPTA.2017.8310146.
[5] SHIEH W Y, HSU C C J, WANG T H. Vehicle positioning and trajectory tracking by infrared signal-direction discrimination for short-range vehicle-to-infrastructure communication systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 368-379. DOI: 10.1109/TITS.2017.2697041.
[6] CAI Y X, LI L, NI S L, et al. Moving vehicle detection based on dense SIFT and extreme learning machine for visual surveillance[C] // 2015 IEEE International Conference on Robotics and Biomimetics. Piscataway, NJ: IEEE Press, 2015: 1614-1618. DOI: 10.1109/ROBIO.2015.7419002.
[7] CLAUS P, OMAR A M S, PEDRIZZETTI G, et al. Tissue tracking technology for assessing cardiac mechanics: principles, normal values, and clinical applications[J]. JACC: Cardiovascular Imaging, 2015, 8(12): 1444-1460. DOI: 10.1016/j.jcmg.2015.11.001.
[8] UNGI T, ABOLMAESUMI P, JALAL R, et al. Spinal needle navigation by tracked ultrasound snapshots[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(10): 2766-2772. DOI: 10.1109/TBME.2012.2209881.
[9] LI H X, SHEN C H, SHI Q F. Real-time visual tracking using compressive sensing[C] // Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. 2011: 1305-1312.DOI: 10.1109/CVPR.2011.5995483.
[10] 卢湖川, 李佩霞, 王栋. 目标跟踪算法综述[J]. 模式识别与人工智能, 2018, 31(1): 61-76.DOI: 10.16451/j.cnki. issn1003-6059.201801006.
[11] 张灿龙, 唐艳平, 李志欣, 等. 基于二阶空间直方图的双核跟踪[J]. 电子与信息学报, 2015, 37(7): 1660-1666. DOI: 10.11999/JEIT141321.
[12] 黄宏图, 毕笃彦, 查宇飞, 等. 基于笛卡尔乘积字典的稀疏编码跟踪算法[J]. 电子与信息学报, 2015, 37(3): 516-521. DOI: 10.11999/JEIT140931.
[13] ZHOU T R, OUYANG Y N, WANG R, et al. Particle filter based on real-time compressive tracking[C] // 2016 International Conference on Audio, Language and Image Processing. Piscataway, NJ: IEEE Press, 2016: 754-759. DOI: 10.1109/ICALIP.2016.7846666.
[14] YUN X, JING Z L, XIAO G, et al. A compressive tracking based on time-space Kalman fusion model[J]. Science China: Information Sciences, 2016, 59(1): 012106. DOI: 10.1007/s11432-015-5356-0.
[15] WEN C B, CAI Y Z, LIU Y R, et al. A reduced-order approach to filtering for systems with linear equality constraints[J]. Neurocomputing, 2016, 193: 219-226. DOI: 10.1016/j.neucom.2016.02.020.
[16] IMANI M, BRAGA-NETO U M. Particle filters for partially-observed boolean dynamical systems[J]. Automatica, 2018, 87: 238-250. DOI: 10.1016/j.automatica.2017.10.009.
[17] ALI A, JALIL A, NIU J W, et al. Visual object tracking: classical and contemporary approaches[J]. Frontiers of Computer Science, 2016, 10(1): 167-188.DOI: 10.1007/s11704-015-4246-3.
[18] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C] // 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Los Alamtios, CA: IEEE Computer Society, 2010: 2544-2550. DOI: 10.1109/CVPR.2010.5539960.
[19] HENRIQUES J F, CASEIOR R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C] // Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV. Berlin: Springer, 2012: 702-715. DOI: 10.1007/978-3-642-33765-9_50.
[20] HENRIQUES J F, CASEIOR R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. DOI: 10.1109/TPAMI.2014.2345390.
[21] DANELLJAN M, KHAN F S, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C] // 2014 IEEE Conference on Computer Vision and Pattern Recognition. Los Alamtios, CA: IEEE Computer Society, 2014: 1090-1097. DOI: 10.1109/CVPR.2014.143.
[22] DANELLJAN M,HÄGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking[C] // Proceedings of the British Machine Vision Conference. Surrey: BMVC Press, 2014: 1-11. DOI: 10.5244/C.28.65.
[23] LI Y, ZHU J K, HOI S C H. Reliable patch trackers: robust visual tracking by exploiting reliable patches[C] // 2015 IEEE Conference on Computer Vision and Pattern Recognition. Los Alamtios, CA: IEEE Computer Society, 2015: 353-361. DOI: 10.1109/CVPR.2015.7298632.
[24] GÖNEN M, ALPAYDIN E. Multiple kernel learning algorithms[J]. Journal of Machine Learning Research, 2011, 12: 2211-2268.
[25] FELZENSAWALB P F, GIRSHICK R B,McALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645. DOI: 10.1109/TPAMI.2009.167.
[26] VAN DE WEIJER J, SCHMID C, VERBEEK J, et al. Learning color names for real-world applications[J]. IEEE Transactions on Image Processing, 2009, 18(7): 1512-1523. DOI: 10.1109/TIP.2009.2019809.
[27] WU Y, LIM J W, YANG M H. Online object tracking: a benchmark[C] // 2013 IEEE Conference on Computer Vision and Pattern Recognition. Los Alamtios, CA: IEEE Computer Society, 2013: 2411-2418. DOI: 10.1109/CVPR.2013.312.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	PANG Lin-na, QIU Yan-yan, LU Jia-kuan. On Some Sufficent Conditions of p-Nilpotency of Finite Groups[J]. Journal of Guangxi Normal University(Natural Science Edition), 2014, 32(2): 64 -66 .
[2]	XU Lunhui, CHEN Kaixun. Prediction of Road Network Speed Distribution Based on BP Neural Network Optimization by Improved Firefly Algorithm[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(2): 1 -8 .
[3]	LI Zhaomei, LI Wenlin, MENG Anxin, ZHAO Zhenchi, QIN Yongfu, LAN Haihui, LU Huijin, CHEN Lisha, LIANG Weigang, SHEN Hongtao. Study of ¹⁴C-AMS in Dating of the Ancient Painting[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(2): 38 -43 .
[4]	XU Tingting, YU Qiuping, QI Peiyi, LIU Kehui, LI Yi, JIANG Yongrong, YU Fangming. Effects of Different Washing Solutions on the Desorption of Heavy Metals from a Lead-zinc Mine Soil[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(2): 188 -193 .
[5]	HAN Bowen. Demand Responsive Transit Scheduling Method Considering Real-time Demand[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(3): 9 -20 .
[6]	WU Juan, ZHU Hongyang, MEI Ping, CHEN Wu, LI Zhongbao. Polymethyl Methacrylate Modified Nano-Silica and Its Stabilizing Effect on Pickering Emulsion[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(3): 120 -131 .
[7]	DU Xuesong,LIN Yong,LIANG Guokun,HUANG Yin,BIN Shiyu, CHEN Zhong,QIN Junqi,ZHAO Yi. Comparison of Low Temperature Resistant Performance between Two Tilapia[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(3): 174 -179 .
[8]	LI Feiyu, WENG Xiaoxiong, YAO Shushen. Research on the Scaling Law Based on the Travel Time Interval of Passengers’ Group[J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(1): 1 -9 .
[9]	XIANG Qinqin, LIAO Zhixian, LI Tinghui, JIANG Pinqun, HUANG Guoxian. Research on Modeling and Control of Photovoltaic Microgrid Inverter under Random Disturbance of Power Grid[J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(1): 19 -25 .
[10]	ZHAO Xin , SONG Yingqiang, HU Yueming, LIU Yilun, ZHU Axing. Optimizing Spatial Distribution of Residential Areas by Using Multi-Source Open Data[J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(1): 26 -40 .

Block Target Tracking Based on Kernel Correlation Filter and Feature Fusion

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 1

Metrics

Comments

Recommended 10