Research Article
Discriminative Fusion Correlation Learning for Visible and Infrared Tracking
Algorithm 1
Discriminative fusion correlation learning for visible and infrared tracking.
Input: The -th visible and infrared images | |
For = 1 to number of frames do | |
1. Crop the samples and extract the -th ( = 1, · · ·, ) channel features for visible and | |
infrared images, respectively. | |
2. Compute the discriminative correlation scores using Eq. (9). | |
3. Compute the fusion correlation scores using Eq. (10). | |
4. Obtain the tracking result by maximizing . | |
5. Extract the -th ( = 1, · · ·, ) channel feature of the target samples and the -th | |
( = 1, · · ·, ) sample . | |
6. Update the discriminative correlation filters and using Eq. (6) and Eq. (8), | |
respectively. | |
end for | |
Output: Target result and the discriminative correlation filters and |