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Dual Model Learning Combined With Multiple Feature Selection for Accurate Visual Tracking
- Source :
- IEEE Access, Vol 7, Pp 43956-43969 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Over these years, object tracking algorithms combined with correlation filters and convolutional features have achieved excellent performance in accuracy and real-time speed. However, tracking failures in some challenging sequences are caused by the insensitivity of deeper convolutional features to target appearance changes and the unreasonable updating of correlation filters. In this paper, we propose dual model learning combined with multiple feature selection for accurate visual tracking. First, we fuse the handcrafted features with the multi-layer features extracted from the convolutional neural network to construct a correlation filter learning model, which can precisely localize the target. Second, we propose an index named hierarchical peak to sidelobe ratio (HPSR). The fluctuation of HPSR determines the activation of an online classifier learning model to redetect the target. Finally, the target locations predicted by the dual learning models mentioned above are combined to obtain the final target position. With the help of dual learning models, the accuracy and performance of tracking have been greatly improved. The results on the OTB-2013 and OTB-2015 datasets show that the proposed algorithm achieves the highest success rate and precision compared with the 12 state-of-the-art tracking algorithms. The proposed method is better adaptive to various challenges in visual object tracking.
- Subjects :
- General Computer Science
Computer science
Dual model
business.industry
General Engineering
multiple feature selection
020206 networking & telecommunications
Pattern recognition
Feature selection
Convolutional neural network
02 engineering and technology
correlation filter
Video tracking
0202 electrical engineering, electronic engineering, information engineering
Eye tracking
020201 artificial intelligence & image processing
General Materials Science
learning models
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
Classifier (UML)
lcsh:TK1-9971
object tracking
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....95c3929627e0c3a95a1654277113d0cb