Research On Joint Appearance Modeling Method Based On Visible And Infrared Target Tracking System

Target tracking technology as important research content is about automation control,computer vision,intelligent information processing and other disciplines,the research results can be applied successfully by intelligent driving,safety monitoring,medical diagnosis,automated production and other aspects which is an great significance to promote the development of social economy.In daily life scenes such as bad lighting,bad weather and other scene changes at any time that brings great challenge to the traditional visible target tracking.More and more attention has been paid to the joint visible and infrared target tracking method to overcome these challenges,the reason is that the technology can make full use of different sensor devices to gather the information redundant complementary characteristics to promote the robustness of target tracking in complex scenarios.But infrared and visible light are two different modes which are significant differences in imaging methods,so how to break the modal differences to highlight its internal connection and become the key to information complementarity.For this reason,this paper draws lessons from the traditional visible target tracking mainstream method to deeply study the appearance modeling method based on muti-source data fusion and effectively realize the complementary fusion of visible and infrared information.The main research content and innovation of this paper include::1.Considering the data bias in the visible and infrared video sequence,a joint appearance modeling method based on canonical correlation analysis and inverse sparse representation is proposed.The method is constructed based on canonical correlation analysis and inverse sparse representation for joint coding model while the candidate targets in visible and infrared video sequences are encoded concurrently.Through a series of simulation experiments,it is proved that the joint coding by exploring the similarity between visible and infrared video sequences in a common subspace to highlight the commonality between the target characteristics that improve the robustness of the model in severe weather conditions.2.In view of the fact that only some features are reliable due to occlusion of target features,a joint appearance modeling method based on collaborative coding is proposed.The modeling method introduces multi-view linear discriminant analysis in the inverse sparse representation model,through the linear projection matrix updated online that enable the intra-class variance of the visible and infrared target feature matrix in the inverse sparse representation is minimized,and the inter-class variance is maximized.A large number of simulation experiments have proved that this method is beneficial for extracting some identifiable information in infrared and visible light video for inverse sparse encoding,thereby improving the robustness of visible and infrared joint target tracking robustness in complex scenarios.3.In order to improve the speed of visible and infrared joint tracking,a appearance modeling method of correlated filtering based on bimodal constraints is proposed.The method adds a bimodal regularization constraint to the spatial regularization correlation filter tracking framework,by constraining the spatial correlation in different modes,a new multi-source joint correlation filter is trained,so that the regression parameters of visible light and infrared images can achieve complementary advantages.