چکیده مقاله
Although the correlation filter CF based trackers have currently achieved brilliant results in terms of both accuracy and robustness, they are not capable of effectively handle the scale variation To tackle this problem and solve the drifting issue, we propose a CF based tracker by selecting and fusion of multiple scales, multiple features, and multiple templates Firstly, to deal with the problems of the fixed template size, a set of possible scales is considered to estimate the scale of a target object Secondly, in order to relieve the drifting issue, a set of candidate templates, which are affected by significant appearance changes is carefully selected and learned as filter templates to jointly capture the target appearance variation Finally, the HOG and the color naming features are integrated to improve the overall tracking performance The experiments are done on CVPR2013 dataset The proposed tracker successfully tracked the target objects in experimented sequences and performed well in terms of accuracy and robustness through the state of the art trackers
کلیدواژهها
نویسندگان
Shadi Shanesazzadeh
Iran University of Science and Technology
Karim Mohammadi
Iran University of Science and Technology
شیوه ارجاع
Shanesazzadeh, Shadi and Mohammadi, Karim,1398,Multi-scale Kernel Correlation Filters for Visual Tracking with Fusion of multi features and multi templates,The 7th National Conference on Computer Science and Engineering and Information Technology - January 2019,Babol
ارائهشده در
مجموعه مقالات هفتمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات3 مرداد 1398 · بابل