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)

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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"

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