Research On Long-term Tracking Algorithm Of UAV Aerial Photography Object

UAV aerial photography object tracking is a key technology for UAV’s intelligent applications,such as aerial reconnaissance,automatic camera,traffic patrol,aircraft tracking,etc.It is of great significance to the development of the UAV industry.Combined with the current research status of object tracking algorithm,this paper adopts the correlation filter which is more in line with the characteristics of UAV visual processing as the core tracking algorithm.At the same time,in order to adapt to the complex scene of UAV aerial photographing,the tracking algorithm is improved.And the design of long-term tracking algorithm for UAV aerial photography object is completed from four aspects: constructing the robust appearance of the object,alleviating the boundary effect,preventing the filter degradation,and object re-detection.The main contents of this paper are as follows:(1)A tracking algorithm based on complementary classifier and multi feature fusion is proposed,which is improved from the direction of building robust appearance.Color histogram is used in the Bayesian classifier,Multiple features are used in the correlation filter.Combining the advantages of various features,the robust appearance of the object is constructed to adapt to complex scenes.According to the response distance of the two classifiers and the response reliability of Bayesian classifier,the two classifiers are fused adaptively.Experimental results show that this method is effective in improving tracking performance.(2)On the basis of constructing the robust appearance of object,a tracking algorithm based on model adaptive spatio-temporal regularization is proposed.The adaptive spatio-temporal regularization term is added to the correlation filter,and the concepts of global and local response variation are introduced.The spatial regularization adds local response variation to alleviate boundary effects while prevent the filter from learning unreliable appearance.The time regularization adds global response variation and initial filter constraint,adaptively to adjusts the filter learning speed,while limiting the update range of the filter to prevent filter degradation.Since the added spatio-temporal regularization term destroys the closed solution of the model,ADMM is used to optimize the filter parameters.The filter model uses a sparse update method to improve the speed of the algorithm;the Bayesian classifier uses an adaptive update method to prevent more noise from learning.Experimental results show that compared with 10 classical algorithms,the proposed algorithm achieves better tracking effect under the premise of realtime performance.(3)On the basis of the above,a long-term UAV tracking algorithm based on re-detection is proposed by adding re-detection module.The module includes two parts: tracking uncertainty detection and object recovery.In the tracking process,the tracking uncertainty is detected.When the tracking result is detected to be unreliable,multiple correlation filters maintained in the object recovery are used to recover the lost object.Experimental results show that the proposed algorithm can effectively solve the problems of severe occlusion and out of view,and meet the real-time needs.