Learning Soft-Consistent Correlation Filters for RGB-T Object Tracking
To track objects efficiently and effectively in adverse illumination conditions even in dark environment,this paper presents a novel soft-consistent correlation filters(SCCF)using RGB and thermal infrared(RGB-T)data for visual tracking.The proposed SCCF uses soft consistency to take both collaboration and heterogeneity into account for joint learning of the correlation filters of RGB and thermal spectra,while the computational time is reduced significantly by employing the Fast Fourier Transform(FFT).Moreover,a novel weighted fusion mechanism is proposed to compute the final response map in the detection phase.Extensive experiments on the benchmark dataset show that the proposed approach performs favorably against state-of-the-art methods,while runs at 50 frames per second.
Visual tracking RGB and thermal fusion Correlation filter Soft-consistent
Yulong Wang Chenglong Li Jin Tang
School of Computer Science and Technology,Anhui University,Hefei,China School of Computer Science and Technology,Anhui University,Hefei,China;Center for Research on Intell
国际会议
广州
英文
295-306
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）