@Article{jai.2021.010455,
AUTHOR = {Yu Wang},
TITLE = {Hybrid Efficient Convolution Operators for Visual Tracking},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {63--72},
URL = {http://www.techscience.com/jai/v3n2/42531},
ISSN = {2579-003X},
ABSTRACT = {Visual tracking is a classical computer vision problem with many 
applications. Efficient convolution operators (ECO) is one of the most outstanding 
visual tracking algorithms in recent years, it has shown great performance using 
discriminative correlation filter (DCF) together with HOG, color maps and 
VGGNet features. Inspired by new deep learning models, this paper propose a 
hybrid efficient convolution operators integrating fully convolution network (FCN) 
and residual network (ResNet) for visual tracking, where FCN and ResNet are 
introduced in our proposed method to segment the objects from backgrounds and 
extract hierarchical feature maps of objects, respectively. Compared with the 
traditional VGGNet, our approach has higher accuracy for dealing with the issues 
of segmentation and image size. The experiments show that our approach would 
obtain better performance than ECO in terms of precision plot and success rate 
plot on OTB-2013 and UAV123 datasets.},
DOI = {10.32604/jai.2021.010455}
}