http://iet.metastore.ingenta.com
1887

Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association

Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The goal of multiple object tracking (MOT) is to estimate the locations of objects and maintain their identities consistently to yield their individual trajectories. MOT has been developed enormously, but it is still a challenging work due to similar appearances of different objects and occlusion by other objects or background in a complex scene. In this study, the authors propose confidence score-based appearance model learning and hierarchical data association for MOT. First, the confidence score is used to divide associated tracklet-detection in the first stage data association into confident and unconfident results, and in the second stage, data association is applied to unconfident tracklet-detection to improve the performance. Furthermore, it can be employed to enhance the robustness of the appearance model and due to the fast confidence score calculation, it can balance the accuracy and processing time. The experimental results with challenging public datasets show distinct performance improvement over other state-of-the-art methods and demonstrate the effect of the authors’ method for online MOT.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2018.5499
Loading

Related content

content/journals/10.1049/iet-cvi.2018.5499
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address