GOMT: Multispectral video tracking based on genetic optimization and multi-features integration

Traditional visual tracking algorithms on RGB cannot effectively distinguish an object from background with similar colour feature, resulting in the migration problem in the tracking process. In order to solve this problem, a tracking algorithm with spectral information is proposed by using band selection, information fusion, and deep features. Firstly, the correlation analysis of 16 spectral band information is carried out, and genetic optimization method is introduced to obtain two bands with low correlation coefficient and abundant information. Moreover, under the framework of kernel correlation filtering tracking, guided filtering is adopted to fuse the information referring to the target region of the two bands. Besides, the feature maps are generated by histogram of gradient and pretrained visual geometry group network. Finally, target is detected via finding the maximum value of a strong response. The proposed genetic optimization based multifeature tracker is compared with multiple tracking methods including correlation filtering and deep learning. Experimental results with multiple groups of spectral videos and corresponding RGB videos demonstrate that the genetic optimization based multifeature tracker method achieves good results in subjective vision and objective evaluation.