The invention discloses a video saliency detection method based on Bayesian fusion and mainly solves the problem that an existing video saliency detection method cannot detect small targets. The implementation scheme is that 1) extracting a video sequence static boundary probability saliency map, a color mean value saliency map and a color contrast saliency map, and carrying out weight fusion to generate a static saliency map; 2) extracting a video sequence dynamic boundary probability saliency map, a PCA prior saliency map and a background prior saliency map, and carrying out weight fusion togenerate a dynamic saliency map; and 3) carrying out fusion on the static saliency map and the dynamic saliency map through a Bayesian model to obtain a video sequence saliency map. Compared with a conventional video saliency algorithm, the method enhances characteristic space and time representation ability, reduces influence of complex backgrounds on the detection effect, can detect the small targets in a video effectively, and can be used for early-stage pretreatment of video target tracking and video segmentation.