MATEC Web Conf.
Volume 128, 20172017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)
|Number of page(s)||4|
|Section||Signal & Image Processing|
|Published online||25 October 2017|
Adaptive Scale Compressive Tracking with Feature Integration
1 The school of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shanxi, China
2 Xi’an Jiaotong University Suzhou Academy, Suzhou, Jiangsu, China
* Corresponding author: email@example.com
Compressive tracking (CT) is utilized to cope with real-time tracking, which use a very sparse measurement matrix to compressive samples of targets and background, then a classifier is trained to distinguish foreground and background. However, this algorithm suffers from the drifting problem, and used the fixed size tracking box to detect, recognize, and update the samples and classifier. In order to solve these problems, we adopt a different way to extracted positive samples, and employ powerful features to exploit the advantages of feature fusion to describe target, a scale pyramid is used to realize adaptive scale tracking. Experimental results on various benchmark video sequences demonstrate the superior performance of our algorithm.
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.