Research On Infrared Target Detection And Tracking Method Based On Artificial Immune Theory

With the rapid development of artificial intelligence,infrared target detection and tracking technology based on computer vision plays an important role in intelligent video monitoring,fire analysis and rescue,criminal investigation site investigation,military target investigation and other fields.In order to detect and track infrared target stably and efficiently,this paper proposes an infrared target detection and tracking method based on artificial immunity theory,and realizes the integrated autonomous operation of infrared target detection and tracking.The specific research contents are as follows:(1)This paper describes the significance,background and current status of infrared target detection and tracking technology,and introduces the theoretical basis of biological immune system and artificial immune system.Four classical artificial immune algorithms including immune genetic algorithm,immune particle swarm optimization algorithm,chaotic immune algorithm and negative selection algorithm are analyzed,and the above algorithms are applied to infrared target detection.The advantages and disadvantages of the algorithm are analyzed according to the actual effect of detection.The analysis shows that the detection performance of the negative selection algorithm is adjustable,suitable for distributed detection,and has a good ability to suppress background interference of infrared images.However,the efficiency of the detector is low,and the detection performance needs to be improved.(2)Aiming at the problems of small imaging area and low signal-to-noise ratio of infrared target,an infrared target detection algorithm based on artificial immune theory is proposed to improve the detection accuracy.Combining the extension theory with the negative selection algorithm,the extension detector is constructed to improve the detection coverage of the negative selection algorithm.Combining with the characteristics of infrared video sequence,two immune initial segmentation methods based on single frame infrared image and multi frame infrared image are designed respectively,which makes the infrared target detection algorithm proposed in this paper has a wider range of application.(3)Aiming at the problem of low success rate of infrared target tracking caused by the fixed scale and poor anti occlusion performance of KCF algorithm,this paper designs the background constraint mechanism of infrared image based on the suppression principle of lnc RNA to virus in immune system.The proposed mechanism can effectively improve the tracking effect when the infrared target scale changes and the target occlusion occurs in the field of view.Experimental results show that the improved algorithm has obvious advantages over the original KCF algorithm in tracking accuracy and stability.(4)In view of the actual demand of infrared target detection and tracking integration in specific scenes,this paper proposes an integrated method of infrared target detection and tracking based on artificial immune theory.The cooperative working mechanism of infrared target detection module and tracking module is studied,and the autonomous cooperative operation of infrared target detection and tracking in some specific application scenarios,such as non-uniform occlusion motion,is realized.