Infrared Target Detection Algorithm under Complex Ground Background

A random sampling mean-shift clustering algorithm based on frame difference light flow was proposed. Firstly, the moving target region was extracted by frame difference method, and the moving region was calculated by optical flow, and the moving target was accurately extracted by adaptive optical flow threshold segmentation method. Then, the connected region labeling algorithm was used to preliminarily divide the moving region, and several connected domain subset eigenvector sample points were obtained. The sampling times of sample points in the subset space were determined by the random sampling strategy proposed. At last, mean shift algorithm was used to carry out several sampling calculations of sample points in each subset, and analyzed whether the clustering convergence results were the same. This strategy improves the detection speed and accuracy of the target by reducing the sampling times of all sample points of the marked results. Experimental results in different infrared test scenarios show that, compared with the traditional infrared multi-target detection algorithm, the method in this paper has good local anti-blocking, accuracy and real-time performance, and the detection rate can reach 95. 27%, and the processing time per frame reaches 39. 12 ms, which meets the real-time processing needs.