Video significance detection method and system based on time-space constraints

Abstract

The invention is applied to the field of video detection, and provides a video significance detection method. The method comprises the steps that superpixel segmentation is conducted on a to-be-detected current frame to obtain a current frame after superpixel segmentation, optical flow field motion estimation is calculated according the current frame and a previous frame, motion distribution energy and motion edge energy are calculated, motion history energy is calculated according to the current frame and the previous frame, and a hybrid motion energy diagram is generated according to the above-mentioned features and a significance diagram of the previous frame; and an initial target segmentation area of the hybrid motion energy diagram is obtained, a reliable target area and a reliable background area are extracted, and a significance global optimization model is constructed according to the reliable target area, the reliable background area and the hybrid motion energy diagram and then solved to obtain a significance diagram of the current frame. According to the video significance detection method, by adopting multiple motion features and space features such as the motion distribution energy of an area layer, the motion edge energy of an edge layer, the motion history energy of a pixel layer and the significance diagram of the previous frame, and the robustness and stability of significance detection are enhanced.

Description

technical field [0001] The invention belongs to the field of video technology, and in particular relates to a video saliency detection method and system based on time-space constraints. Background technique [0002] Saliency detection aims to predict relatively noticeable areas visually. It has a wide range of applications in video classification, video retrieval, video summarization, scene understanding, object tracking and other fields. It is the foundation and key issue of computer vision. Since motion information is an important clue for video saliency detection, unlike static image saliency detection which only considers spatial information, video saliency detection should consider both motion information and spatial information. [0003] How to extract the motion information of salient objects is a key issue in video saliency detection. At present, most methods use the optical flow field to estimate the motion of the salient target, but the optical flow field is very ...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a video saliency detection method and system based on spatio-temporal constraints, aiming to solve the problem of insufficient robustness of existing video saliency detection methods in complex scenes

Images