TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Structurally Enhanced Correlation Tracking |
| 영문제목(English Title) |
Structurally Enhanced Correlation Tracking |
| 저자(Author) |
Mayur Rajaram Parate
Kishor M. Bhurchandi
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| 원문수록처(Citation) |
VOL 11 NO. 10 PP. 4929 ~ 4947 (2017. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness. |
| 키워드(Keyword) |
Visual tracking
object tracking
correlation filter
computer vision
target representation
target scale estimation
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| 파일첨부 |
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