As infrared dim and small target is difficult to detect and track under complicated background,an improved fast detection method for dim and small infrared targets is proposed.Firstly,the background noise is suppressed by the improved morphological filtering,and the target pixel is highlighted preliminarily by the variance estimation of the processed multi-frame image,then the SNR is estimated to get the whole image sequence pixel score.The pixels with high scores are marked as the target pixels,and the marked image is divided and analyzed.Finally,the target pixels in the continuous image sequence is extracted accurately.The target pixels are treated as the input of the target tracking algorithm of the Hough transform,and the double thresholds are set to achieve the effective tracking of the target.The experimental results show that the target detection based on the noise variance estimation has a high detection probability and a low false alarm rate in the infrared dim and small target extraction under complex background,and the target pixels obtained as the input of the Hough transform can effectively track the target and simplify the complexity of the algorithm to achieve rapid extraction and tracking of targets,and it has a high application value.