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Object Tracking by Unified Semantic Knowledge and Instance Features

Suofei ZHANG, Bin KANG, Lin ZHOU

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Summary :

Instance features based deep learning methods prompt the performances of high speed object tracking systems by directly comparing target with its template during training and tracking. However, from the perspective of human vision system, prior knowledge of target also plays key role during the process of tracking. To integrate both semantic knowledge and instance features, we propose a convolutional network based object tracking framework to simultaneously output bounding boxes based on different prior knowledge as well as confidences of corresponding Assumptions. Experimental results show that our proposed approach retains both higher accuracy and efficiency than other leading methods on tracking tasks covering most daily objects.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.3 pp.680-683
Publication Date
2019/03/01
Publicized
2018/11/30
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDL8181
Type of Manuscript
LETTER
Category
Image Recognition, Computer Vision

Authors

Suofei ZHANG
  Nanjing University of Posts and Telecommunications
Bin KANG
  Nanjing University of Posts and Telecommunications
Lin ZHOU
  Southeast University

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