期刊
INFRARED PHYSICS & TECHNOLOGY
卷 98, 期 -, 页码 69-81出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.infrared.2019.02.012
关键词
Thermal infrared object tracking; Continuous correlation filters; Adaptive feature fusion; Average peak-to-correlation energy
Thermal infrared (TIR) object tracking is one of the most challenging tasks in computer vision. This paper proposes a robust TIR tracker based on the continuous correlation filters and adaptive feature fusion (RCCF-TIR). Firstly, the Efficient Convolution Operators (ECO) framework is selected to build the new tracker. Secondly, an optimized feature set for TIR tracking is adopted in the framework. Finally, a new strategy of feature fusion based on average peak-to-correlation energy (APCE) is employed. Experiments on the VOT-TIR2016 (Visual Object Tracking-TIR2016) and PTB-TIR (A Thermal Infrared Pedestrian Tracking Benchmark) dataset are carried out and the results indicate that the proposed RCCF-TIR tracker combines good accuracy and robustness, performs better than the state-of-the-art trackers and has the ability to handle various challenges.
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