Aiming at the lack of target texture information in infrared target tracking, strong coupling between the target and the background, and the case of occlusion causing that the characteristic information cannot be extended, the anti-occlusion algorithm for infrared target tracking with salient feature space was proposed. Firstly, by analyzing the characteristics of the infrared target, the salient feature space was generated by using multi-scale saliency, contrast and information entropy, and the regions were clustered with the super-pixel feature distance and spatial distance to highlight the infrared target area. By quantifying the different regions of the infrared image, the saliency map was generated. Based on the saliency map and the original image, a plurality of target candidate regions was generated as the tracking algorithm′s input. Finally, the spatial distribution field matrices of the infrared target were matched by way of the global candidate regions. At the same time, the occlusion detection mechanism on account of the change of the salience regions and the inter-frame variation curve of feature similarity was established. Experimental results on different IR test sets show that the proposed infrared target algorithm can achieve better tracking performance under the occlusion condition, which effectively enhances the robustness of the tracking algorithm.