The invention discloses a video significance detecting method based on area segmentation, wherein the method mainly settles a problem of low detecting accuracy by an existing video significance detecting method. The video saliency detecting method comprises the steps of 1, performing
linear iteration clustering on video frames, thereby obtaining a super-pixel block, and extracting the static characteristic of the super-pixel block; 2, by means of a variational
optical flow method, obtaining the dynamic characteristic of the super-pixel block; 3, fusing the static characteristic and the dynamic characteristic for obtaining a
characteristic matrix, and performing K-means clustering on the
characteristic matrix; 4, performing
linear regression model training on each cluster, thereby obtaining a regression model; and 5, reconstructing a mapping relation between a
test set sample and a obtaining the significance value of a
test set super-pixel block, and furthermore obtaining the significance graph of a testing sequence. Compared with a traditional video significance
algorithm, the video significance detecting method has advantages of improving
characteristic space and time representation capability, and reducing effect of illumination to detecting effect. The video significance detecting method can be used for early-period preprocessing of video target tracking and video segmenting.