Infrared imaging is an effective method for detecting gas leaks, enabling dynamic and visual observation of leakage occurrences. However, background interference and the intangible nature of gases often result in infrared images with indistinct gas-plume contours and reduced contrast. This study introduces a segmentation algorithm based on background modeling and density clustering that harnesses the spatiotemporal distribution characteristics of infrared gas images to segment gas regions in low-contrast infrared imagery. The foreground image was extracted by analyzing the matching relationship between the current frame and a sequence of frames using a Gaussian mixture model. Subsequently, a density clustering algorithm was applied to cluster the foreground image with spatial size constraints to filter out low-density regions. Morphological operations were performed to identify the gas-dispersion area. The experimental results indicate that the proposed algorithm can detect and segment low-contrast gas leaks within a scene. It significantly reduces noise and dynamic background interference, addresses voids in the gas region, and demonstrates distinct advantages over other algorithms. This offers a valuable reference for research on the segmentation of infrared images for gas-leak detection.