Infrared Object Detection Based on Improved Twist Tensor Model

Infrared object detection holds significant importance in automatic target search and tracking system under complex background. The conventional structural tensor models have not harnessed the full potential of spatio-temporal domain information in sequence scenes, and the strong edge contours in the image often lead to false alarms. In order to tackle this problem, we propose an improved twist tensor model based on the optimization of background constraints. Firstly, we propose a diffusion function according to the gradient difference between the target and backgrounds, which preserves the target signal to a great extent. Secondly, the spatio-temporal information of the sequence images is used to construct the twist tensor objective constraint optimization function, and the improved twist tensor effectively distinguishes the sparse components of the target and the low-rank components of the background. Finally, the optimized model is solved using ADMM to obtain the final target signal. Eight sequence images and nine comparison methods are performed for experimental validation, after improvement, the mean SSIM value reaches 0.9921, the mean BSF value attains 126.1710, and the detection rate also surpasses 85%, experiment results demonstrate that the proposed algorithm can effectively suppress the complex background while retaining the target well.