Mean shift object tracking based on center symmetric-local similarity number model

doi:10.6046/gtzyyg.2016.03.07

Abstract
References
Related Articles
Metrics

Download: PDF(6209 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In order to complete the task of real-time object tracking, this paper proposes a new local texture description operator, which is called center symmetric-local similarity number(CS-LSN). The operator analyzed the center pixel and its eight neighborhood pixels, on the basis of the local similarity number (LSN); as the texture operator fails to distinguish the same local saliency of different texture structures, the authors added the local gradient information using the center pixel as the symmetric point, extracted the key pixels which correspond only to the five major patterns of the CS-LSN in the target candidate region, effectively restrained the influence of the background pixels and reduced the computation in the target representation model, and then represented target by CS-LSN texture feature and the chromaticity in the true target pixel, which was embedded into the mean shift(MS) tracking framework. The experimental results show that the proposed method can continuously track the target when the background and the color are similar under the condition of changing illumination and in the occlusion cases. The processing speed can reach 25 frames or so per second when the object is 29 pixel×25 pixel, so it can satisfy the demand of real-time application.

Keywords UAV land survey base-map aerial photography DOM

:	TP751.1
	TP391

Issue Date: 01 July 2016

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	BI Kai
	HUANG Shaolin

Cite this article:

BI Kai,HUANG Shaolin. Mean shift object tracking based on center symmetric-local similarity number model[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 37-45.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.03.07 OR https://www.gtzyyg.com/EN/Y2016/V28/I3/37

[1] Maggio E,Cavallaro A.Video Tracking:Theory and Practice[M].London,UK:Queen Mary University of London,2012.
[2] Comaniciu D,Ramesh V,Meer P.Kernel-based object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5):564-577.
[3] 杨欣,费树岷,李刚,等.基于复杂特征融合的改进mean shift目标跟踪[J].控制与决策,2014,29(7):1297-1300. Yang X,Fei S M,Li G,et al.Improved mean shift tracking algorithm based on complicated feature fusion[J].Control and Decision,2014,29(7):1297-1300.
[4] Vojir T,Noskova J,Matas J.Robust scale-adaptive mean-shift for tracking[J].Pattern Recognition Letters,2014,49:250-258.
[5] Li S X,Wu O,Zhu C F,et al.Visual object tracking using spatial context information and global tracking skills[J].Computer Vision and Image Understanding,2014,125:1-15.
[6] 董文会,常发亮,李天平.融合颜色直方图及SIFT特征的自适应分块目标跟踪方法[J].电子与信息学报, 2013,35(4):770-776. Dong W H,Chang F L,Li T P.Adaptive fragments-based target tracking method fusing color histogram and SIFT features[J].Journal of Electronics and Information Technology,2013,35(4):770-776.
[7] Maenpaa T,Pietikainen M.Texture analysis with local binary patterns[M]//Handbook of Pattern Recognition and Computer Vision.USA:World Scientific Pub.Co.Inc,2005:197-216.
[8] Ning J F,Zhang L,Zhang D,et al.Robust object tracking using joint color-texture histogram[J].International Journal of Pattern Recognition and Artificial Intelligence,2009,23(7):1245-1263.
[9] 张涛,蔡灿辉.基于多特征Mean Shift的人脸跟踪算法[J].电子与信息学报,2009,31(8):1816-1820. Zhang T,Cai C H.A face tracking algorithm based on multiple feature mean shift[J].Journal of Electronics & Information Technology,2009,31(8):1816-1820.
[10] 张炯,宁记峰,颜永丰,等.对立色LBP模型的目标跟踪[J].中国图象图形学报,2012,17(11):1418-1424. Zhang J,Ning J F,Yan Y F,et al.Object tracking with opponent color LBP model[J].Journal of Image and Graphics,2012,17(11):1418-1424.
[11] Borji A,Itti L.State-of-the-art in visual attention modeling[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):185-207.
[12] 黎万义,王鹏,乔红.引入视觉注意机制的目标跟踪方法综述[J].自动化学报,2014,40(4):561-576. Li W Y,Wang P,Qiao H.A survey of visual attention based methods for object tracking[J].Acta Automatica Sinica,2014,40(4):561-576.
[13] Tavakoli H R,Moin M S,Heikkila J.Local similarity number and its application to object tracking[J].International Journal of Advanced Robotic Systems,2013,10:184.
[14] 张红颖,胡正.融合局部三值数量和色度信息的均值漂移跟踪[J].电子与信息学报,2014,36(3):624-630. Zhang H Y,Hu Z.Mean Shift tracking method combing local ternary number with hue information[J].Journal of Electronics and Information Technology,2014,36(3):624-630.
[15] Heikkila M,Pietikainen M,Schnid C.Description of interest regions with local binary patterns[J].Pattern Recognition,2009,42(3),425-436.
[16] 陈东成,朱明,高文,等.在线加权多示例学习实时目标跟踪[J].光学精密工程,2014,22(6):1661-1667. Chen D C,Zhu M,Gao W,et al.Real-time object tracking via online weighted multiple instance learning[J].Optics and Precision Engineering,2014,22(6):1661-1667.
[17] Kalal Z,Mikolajczyk K,Matas J.Tracking-learning-detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[18] 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.

[1]	WU Fang, LI Yu, JIN Dingjian, LI Tianqi, GUO Hua, ZHANG Qijie. Application of 3D information extraction technology of ground obstacles in the flight trajectory planning of UAV airborne geophysical exploration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 286-292.
[2]	REN Chaofeng, PU Yuchi, ZHANG Fuqiang. A method for extracting match pairs of UAV images considering geospatial information[J]. Remote Sensing for Natural Resources, 2022, 34(1): 85-92.
[3]	ZHAO Xiaowei, HUANG Yang, WANG Yongqiang, CHU Ding. Estimation of maize seedling number based on UAV multispectral data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 106-114.
[4]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[5]	LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[6]	ZHOU Chaofan, GONG Huili, CHEN Beibei, LEI Kunchao, SHI Liyuan, ZHAO Yu. Prediction of land subsidence along Tianjin-Baoding high-speed railway using WT-RF method[J]. Remote Sensing for Natural Resources, 2021, 33(4): 34-42.
[7]	WU Jiang, LIU Chun, YING Shen, YU Ting. Spatial delineation methods of urban areas[J]. Remote Sensing for Natural Resources, 2021, 33(4): 89-97.
[8]	LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[9]	GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[10]	ZHENG Xiongwei, PENG Bei, SHANG Kun. Assessment of the interpretation ability of domestic satellites in geological remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 1-10.
[11]	LIU Yongmei, FAN Hongjian, GE Xinghua, LIU Jianhong, WANG Lei. Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 11-17.
[12]	BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[13]	LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[14]	ZANG Chuankai, SHEN Fang, YANG Zhengdong. Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 45-53.
[15]	LI Yang, YUAN Lin, ZHAO Zhiyuan, ZHANG Jinlei, WANG Xianye, ZHANG Liquan. Inversion of tidal flat topography based on unmanned aerial vehicle low-altitude remote sensing and field surveys[J]. Remote Sensing for Natural Resources, 2021, 33(3): 80-88.

Viewed

Full text

Abstract

Cited

Shared

Discussed