Authors:
Mingjie Liu
;
Cheng-Bin Jin
;
Bin Yang
;
Xuenan Cui
and
Hakil Kim
Affiliation:
Inha University, Korea, Republic of
Keyword(s):
Object Tracking, Kernelized Correlation Filter, Convolutional Features, Scale Variation, Appearance Model Update Strategy.
Abstract:
Considering the recent achievements of CNN, in this study, we present a CNN-based kernelized correlation filter (KCF) online visual object tracking algorithm. Specifically, first, we incorporate the convolutional layers of CNN into the KCF to integrate features from different convolutional layers into the multiple channel. Then the KCF is used to predict the location of the object based on these features from CNN. Additionally, it is worthying noting that the linear motion model is applied when do object location to reject the fast motion of object. Subsequently, the scale adaptive method is carried out to overcome the problem of the fixed template size of traditional KCF tracker. Finally, a new tracking update model is investigated to alleviate the influence of object occlusion. The extensive evaluation of the proposed method has been conducted over OTB-100 datasets, and the results demonstrate that the proposed method achieves a highly satisfactory performance