A Effective Online Training Algorithm by Partitioning Bounding Box for Visual Object Tracking Using Convolutional Neural Network 


Vol. 42,  No. 6, pp. 1117-1128, Jun.  2017
10.7840/kics.2017.42.6.1117


PDF
  Abstract

This paper proposes an effective online training algorithm which resizes bounding box for visual object tracking in Convolutional Neural Network (CNN). The proposed algorithm employs partitioned bounding boxes for resizing bounding box. Compared to previous algorithm where bounding box size is rarely changed, the proposed algorithm improves the efficiency in object tracking by resizing the size of bounding boxes. The proposed algorithm is composed of three classifiers; The front-end classifier is for tracking target object by using the entire feature of previous bounding box, and the other two back-end classifiers are for resizing bounding box when the score acquired by the first classifier is lower than threshold or the number of frame exceeds a determined number. Training data for front-end classifier are extracted from previous raw result, and training data for back-end classifiers king a target in a sequence, the proposed algorithm makes more accurate result and training data of various sizes than previous algorithm by resizing bounding box. Experimental results show that the success rate and the precision for visual object tracking are improved by 3% and 5%, respectively, when compared with previous works.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

I. Kim and S. Hwang, "A Effective Online Training Algorithm by Partitioning Bounding Box for Visual Object Tracking Using Convolutional Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 6, pp. 1117-1128, 2017. DOI: 10.7840/kics.2017.42.6.1117.

[ACM Style]

In-Sung Kim and Sun-Young Hwang. 2017. A Effective Online Training Algorithm by Partitioning Bounding Box for Visual Object Tracking Using Convolutional Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 42, 6, (2017), 1117-1128. DOI: 10.7840/kics.2017.42.6.1117.

[KICS Style]

In-Sung Kim and Sun-Young Hwang, "A Effective Online Training Algorithm by Partitioning Bounding Box for Visual Object Tracking Using Convolutional Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 6, pp. 1117-1128, 6. 2017. (https://doi.org/10.7840/kics.2017.42.6.1117)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880