In view of the problem that the moving target may be occluded or tracked under uncertain conditions during the aerial target tracking of UAV in complex scenes

resulting in the gradual damage

drift

and irreversible failure of the visual model

a long-term tracking algorithm for UAV was proposed. Firstly

the complementary classifier was designed for multi-feature adaptive fusion. The color histogram feature was used in Bayesian classifier

and the directional gradient histogram

grayscale

and color name features were used in the correlation filter. In combination with the advantages of multiple features

the robust appearance of the target was built to adapt to the complex scenes. Secondly

an adaptive spatial-temporal regularization term was added to the correlation filter. Local changes were introduced into the spatial regularization parameters for the implementation of the filter with low pixel credibility during learning. In temporal regularization

the learning of the filter was adaptively adjusted according to the global response

and the initial filter was used to constrain the update range

which effectively prevented filter degradation while mitigating boundary effects. Finally

a re-detection module was added to make sure the accuracy of the tracking process. Experimental results show that the proposed algorithm could adapt to the complex scenes of UAV aerial photography

alleviate boundary effects

and prevent filter degradation. In comparison with similar mainstream algorithms

the proposed algorithm could still meet the real-time requirements and achieve better tracking effect even when the target experienced serious occlusion or moved out of view.