Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (4): 14-23.doi: 10.6040/j.issn.1672-3961.0.2018.461

• Machine Learning & Data Mining • Previous Articles     Next Articles

Object tracking algorithm based on deep residual features and entropy energy optimization

Jinchao HUANG()   

  1. College of Mathematics and Information Engineering, Longyan University, Longyan 364000, Fujian, China
  • Received:2018-10-29 Online:2019-08-20 Published:2019-08-06
  • Supported by:
    福建省中青年教师教育科研项目(JT180523)

Abstract:

To solve the low rate of accuracy, real-time and robustness of object tracking algorithm based on model updating, a new algorithm based on deep residual features and entropy energy optimization was proposed. Deep residual features were first extracted from original video sequence by deep residual network. The entropy energy from deep residual features were calculated, and the deep frequency from entropy energy by two-dimension kernel transformation could be calculated, after that we got the deep balance by deep frequency with differential equation, and then the object state by MLE was estimated, including object position and speed. To validate the feasibility and efficiency of the proposed algorithm, the comparing experiments on the object tracking basis (OTB) dataset for the state-of-the-art algorithms were done, and the comparison results showed that the proposed algorithm had significant improvement on tracking accuracy and robustness. By using entropy energy optimization for deep residual features, the proposed algorithm had more flexibility and robustness for object tracking.

Key words: deep residual network, entropy energy, deep residual features, maximum likelihood estimation, object tracking

CLC Number: 

  • TP391

Fig.1

The common form of residual learning"

Fig.2

The deep residual network model for extracting"

Fig.3

The results of deep residual features extracted by moving objects"

Fig.4

The 32 moving object tracking image sequences in the OTB dataset"

Fig.5

The estimation standard for object tracking"

Fig.6

Comparison curves of accuracy and precision of 32 object tracking seuqnces by algorithms"

Fig.7

Effect results of four common tracking scenes by five different algorithms"

Fig.8

All algorithm for object tracking performance comaprison in all six scences in OTB dataset"

1 魏全禄, 老松杨, 白亮. 基于相关滤波器的视觉目标跟踪综述[J]. 计算机科学, 2016, 43 (11): 1- 5, 18.
doi: 10.11896/j.issn.1002-137X.2016.11.001
WEI Quanlu , LAO Songyang , BAI Liang . Visual object tracking based on correlation filters: a survey[J]. Journal of Computer Science, 2016, 43 (11): 1- 5, 18.
doi: 10.11896/j.issn.1002-137X.2016.11.001
2 包加桐, 宋爱国, 唐鸿儒, 等. 基于视觉目标跟踪的侦察机器人导航方法[J]. 东南大学学报(自然科学版), 2012, 42 (3): 399- 405.
BAO Jiangtong , SONG Aiguo , TANG Hongru , et al. Navigation method for reconnaissance robot based on vision object tracking[J]. Journal of Southeast University (Natural science edition), 2012, 42 (3): 399- 405.
3 张微, 康宝生. 相关滤波目标跟踪进展综述[J]. 中国图象图形学报, 2017, 22 (8): 1017- 1033.
ZHANG Wei , KANG Baosheng . Recent advances in correlation filter-based object tracking: a review[J]. Journal of Image and Graphics, 2017, 22 (8): 1017- 1033.
4 COOKE J R H , TER HORST A C , VAN Beers R J , et al. Effect of depth information on multiple-object tracking in three dimensions: a probabilistic perspective[J]. PLoS Computational Biology, 2017, 13 (7): 100- 109.
5 ONATE J M B , CHIPANTASI D J M , ERAZO N R V . Tracking objects using artificial neural networks and wireless connection for robotics[J]. Journal of Telecommunication, Electronic and Computer engineering (JTEC), 2017, 9 (1/2/3): 161- 164.
6 冯桂兰, 田维坚, 黄昌清, 等. 基于序贯蒙特卡罗的多线索目标跟踪算法[J]. 光电工程, 2010, 37 (8): 5- 11.
doi: 10.3969/j.issn.1003-501X.2010.08.002
FENG Guilan , TIAN Weijian , HUANG Changqing , et al. Object tracking algorithm based on multi-cue and sequential Monte Carlo[J]. Opto-Electronic Engineering, 2010, 37 (8): 5- 11.
doi: 10.3969/j.issn.1003-501X.2010.08.002
7 梁顺健, 汪俊彬, 邬依林. 基于模糊算法的多移动机器人目标跟踪[J]. 自动化与仪表, 2014, 29 (2): 5- 7, 37.
doi: 10.3969/j.issn.1001-9944.2014.02.002
LIANG Shunjian , WANG Junbin , WU Yilin . Fuzzy algorithm of target tracking control for multiple mobile robots[J]. Automation and Instrumentation, 2014, 29 (2): 5- 7, 37.
doi: 10.3969/j.issn.1001-9944.2014.02.002
8 刘文强, 刘志刚, 耿肖, 等. 基于均值漂移和粒子滤波算法的接触网几何参数检测方法研究[J]. 铁道学报, 2015, 37 (11): 30- 36.
doi: 10.3969/j.issn.1001-8360.2015.11.005
LIU Wenqiang , LIU Zhigang , GENG Xiao , et al. Research on detection method for geometrical parameters of catenary system based on mean shift and particle filter algorithm[J]. Journal of the China Railway Society, 2015, 37 (11): 30- 36.
doi: 10.3969/j.issn.1001-8360.2015.11.005
9 JIA C, WANG Z, WU X, et al. A tracking-learning-detection (TLD) method with local binary pattern improved[C]//IEEE International Conference on Robotics and Biomimetics (ROBIO). Waterloo, Canada: IEEE, 2015: 1625-1630.
10 YAO R , SHI Q , SHEN C , et al. Part-based robust tracking using online latent structured learning[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27 (6): 1235- 1248.
doi: 10.1109/TCSVT.2016.2527358
11 BIN OMRAN S , KOMEV I A , BELLAICHE L . Wang-Landau Monte Carlo formalism applied to ferroelectrics[J]. Physical Review b, 2016, 93 (1): 141- 148.
12 刘磊. 基于改进卷积神经网络的在线视觉目标跟踪方法[J]. 内蒙古师范大学学报(自然科学汉文版), 2017, 46 (6): 878- 883.
doi: 10.3969/j.issn.1001-8735.2017.06.026
LIU Lei . Online visual target tracking method based on improved convolution neural network[J]. Journal of Inner Mongolia Normal University(Natural Science Chinese Edition), 2017, 46 (6): 878- 883.
doi: 10.3969/j.issn.1001-8735.2017.06.026
13 LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015, 521 (53): 436- 438.
14 HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 770-778.
15 张冬妍, 李佳佳, 宋现铭. 基于二维熵和粒子群优化的红外检测与跟踪[J]. 计算机工程与设计, 2017, 38 (5): 1296- 1300.
ZHANG Dongyan , LI Jiajia , SONG Xianming . Infrared detection and tracking based on two-dimensional entropy and particle swarm optimization[J]. Computer Engineering and Design, 2017, 38 (5): 1296- 1300.
16 郭学卫, 申永军, 杨绍普. 基于样本熵和分数阶傅里叶变换的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36 (18): 65- 69.
GUO Xuewei , SHEN Yongjun , YANG Shaopu . Application of sample entropy and Fractional fourier transform in the fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2017, 36 (18): 65- 69.
17 FIGUEROA A , LOPEZ J , CASTANOS O , et al. Entropy energy inequalities for qudit states[J]. Journal of Physics a: Mathematical and Theoretical, 2015, 48 (6): 653- 659.
18 HENRIQUES J F , CASEIRO 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
19 DANELLJAN M, HAGER G, KHAN F, et al. Accurate scale estimation for robust visual tracking[C]//British Machine Vision Conference. Nottingham, UK: BMVA Press, 2014: 141-144.
20 DANELLJAN M, HAGER G, SHAHBAZ KHAN F, et al. Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision. Los Angles: IEEE, 2015: 4310-4318.
21 ZHANG J, MA S, SCLAROFF S. MEEM: robust tracking via multiple experts using entropy minimization[C]//European Conference on Computer Vision. Zurich: Springer, 2014: 188-203.
22 HARE S, SAFFARI A, STRUCK P H S T. Structured output tracking with kernels[C]//IEEE International Conference on Computer Vision. Sydney: IEEE, 2012: 263-270.
23 JIA X, LU H, YANG M H. Visual tracking via adaptive structural local sparse appearance model[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. San Diego: IEEE, 2012: 1822-1829.
24 ZHONG W, LU H, YANG M H. Robust object tracking via sparsity-based collaborative model[C]//2012 IEEE Conference on Computer vision and pattern recognition. San Diego: IEEE, 2012: 1838-1845.
25 BAO C, WU Y, LING H, et al. Real time robust l1 tracker using accelerated proximal gradient approach[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. San Diego: IEEE, 2012: 1830-1837.
26 ZHANG K, ZHANG L, YANG M H. Real-time compressive tracking[C]//European Conference on Computer Vision. Heidelberg: Springer, 2012: 864-877.
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