Paper Info
Title
Parallel Correlation Filters for Real-Time Visual Tracking.
Abstract
Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.
Year
DOI
Venue
2019
10.3390/s19102362
SENSORS
Keywords
Field
DocType
visual tracking,parallel correlation filters,reasonable distribution of correlation output,real-time
BitTorrent tracker,Computer vision,Correlation filter,Electronic engineering,Gaussian,Video tracking,Correlation,Eye tracking,Artificial intelligence,Engineering,Merge (version control)
Journal
Volume
Issue
ISSN
19
10
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Yijin Yang111.03
Yihong Zhang2910.65
Demin Li32810.08
Zhijie Wang493.86