Video object extraction is a key technology in intelligence surveillance. An object detection algorithm for low-quality video based on Gaussian Mix Model and stochastic resonance was proposed. Firstly, the algorithm generated the object probability gray image from the current frame with the Gaussian Mix Model by the mapping function defined. Then, stochastic resonance was applied to the object probability gray image by adding noise until the defined evaluation function achieved the minimum value. After stochastic resonance, an effectively enhanced object probability gray image could be obtained. Hence the binary image including the interested objects is retrieved by segmentation of the enhanced object probability gray image. The experimental results show that the proposed algorithm combining the Gaussian Mix Model and the stochastic resonance achieved satisfactory subjective and objective performance under the worse environment with dark, foggy and infrared imaging while the classic background subtraction method almost could not detect the interested objects.