Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | PRCV | Fusion mechanism,Computer vision,Thermal,Computer science,Eye tracking,Video tracking,Correlation,Fast Fourier transform,Frame rate,RGB color model,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulong Wang | 1 | 4 | 4.82 |
| Chenglong Li | 2 | 282 | 34.38 |
| Jin Tang | 3 | 322 | 62.02 |