|Table of Contents|

Structured discriminant sparse representation based object tracking(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年03期
Page:
271-
Research Field:
Publishing date:

Info

Title:
Structured discriminant sparse representation based object tracking
Author(s):
Mao ZhengchongHuang Shuwei
Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University,Wuxi 214122,China
Keywords:
structured sparse representation object tracking scale-invariant feature transform classifiers dictionaries
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.003
Abstract:
An improved algorithm is proposed aiming at such shortcomings of sparse representation based object tracking algorithm as using an overall template and the poor ability of distinguishing targets from a background. Scale-invariant feature transform(SIFT)is used to extract the features of a target. Candidate objects are sparsely represented using appearance models of structured sparse representation,and sparse coefficients are obtained. A discriminant classifier is designed and trained by positive and negative samples,candidate objects are classified,and a confidence value is obtained. The tracking result of the previous frame is used to update the classifier and the dictionary. The improved algorithm is simulated. The average overlap ratio and average center point error of 3 test sequences of the simulation results are calculated,and Deer test sequence’s are 0.633 8 and 9.397 6,Car11 test sequence’s are 0.677 5 and 1.943 3,Caviar2 test sequence’s are 0.753 5 and 3.838 2.

References:

[1] Shu C F,Hampapur A,Lu M,et al. IBM smart surveillance system(S3):An open and extensible framework for event based surveillance[C]//IEEE Conference on Advanced Video and Signal Based Surveillance. Como,Italy:IEEE,2005:318-323. [2]Shah M,Javed O,Shafique K. Automated visual surveillance in realistic scenarios[J]. IEEE Multimedia,2007,14(1):30-39. [3]杜鹃,吴芬芬. 高斯混合模型的运动目标检测与跟踪算法[J]. 南京理工大学学报,2017,41(1):41-46. Du Juan,Wu Fenfen. Movement target tracking algorithm by using Gaussian mixture model[J]. Journal of Nanjing University of Science and Technology,2017,41(1):41-46. [4]许敬,王晓锋. 基于贝叶斯概率的运动目标识别方法[J]. 南京理工大学学报,2013,37(1):80-84. Xu Jing,Wang Xiaofeng. Recognition method of moving target using Bayesian probability theory[J]. Journal of Nanjing University of Science and Technology,2013,37(1):80-84. [5]Zhang Shengping,Yao Hongxun,Sun Xin,et al. Sparse coding based visual tracking:Review and experimental comparison[J]. Pattern Recognition,2013,46(7):1772-1788. [6]张少辉,王迤冉. 用于图像识别的稀疏高斯编码[J]. 南京理工大学学报,2016,40(1):61-66. Zhang Shaohui,Wang Yiran. Sparse Gaussian coding for image recognition[J]. Journal of Nanjing University of Science and Technology,2016,40(1):61-66. [7]Mei Xue,Ling Haibin. Robust visual tracking using 1 minimization[EB/OL]. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5459292,2018-05-12. [8]Bai Tianxiang,Li Y F. Robust visual tracking with structured sparse representation appearance model[J]. Pattern Recognition,2012,45(6):2390-2404. [9]Zhang Qi,Rui Ting,Fang Husheng,et al. Particle filter object tracking based on Harris-SIFT feature matching[J]. Procedia Engineering,2012,29:924-929. [10]白廷柱,侯喜报. 基于SIFT算子的图像匹配算法研究[J]. 北京理工大学学报,2013,33(6):622-627. Bai Tingzhu,Hou Xibao. An improved image matching algorithm based on SIFT[J]. Transactions of Beijing Institute of Technology,2013,33(6):622-627. [11]Babenko B,Yang M H,Belongie S. Visual tracking with online multiple instance learning.[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami,USA:IEEE,2009:983-990. [12]Ross D A,Lim J,Lin R S,et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision,2008,77(1-3):125-141.

Memo

Memo:
-
Last Update: 2018-06-30