Object Segmentation Method for Dynamic Background Based on Motion Saliency Map and Optical Flow Vector Analysis

Abstract

The invention discloses a dynamic background object segmentation method based on a motion saliency map and optical flow vector analysis. The method comprises steps of: firstly extracting the approximate area of a moving object based on the motion saliency map; then obtaining the moving boundary of the moving object and a background region in virtue of an optical flow field between adjacent frames; analyzing the motion saliency map by using the moving boundary to obtain accurate pixels in the moving object; and finally obtaining the image superpixels by using over-segmentation technology and achieving final pixel-level object segmentation by introducing the concept of confidence and establishing an apparent model including various information. The method is tested in a plurality of publically published video sequences and the effectiveness and the superiority of the method are verified by comparison with a conventional method.

Description

technical field [0001] The invention relates to a dynamic background object segmentation method based on motion saliency map and optical flow vector analysis. Background technique [0002] Video sequence moving target segmentation is an important and basic research direction in the field of computer vision, and has a wide range of applications in human-computer interaction, visual navigation, video surveillance, intelligent transportation and other fields. According to whether the camera is moving, it can be divided into static background object segmentation and dynamic background object segmentation. In static background target segmentation, the camera remains still and only the target moves. In this case, it is relatively easy to achieve the segmentation of moving targets. It has been widely used in video surveillance of fixed scenes such as parking lots, squares, and highway traffic. The commonly used method Including frame difference method, mixed Gaussian model, adapti...

AI Technical Summary

Problems solved by technology

Dey et al. proposed a moving object segmentation method based on the fundamental matrix constraint by using the feature point motion trajectory independently extracted and tracked from the video sequence. However, this method only realized the accurate classification of the feature motion trajectory, and did not realize the final pixel-level moving target segmentation

Cui et al. constructed a trajectory matrix containing target and background motion trajectories, and realized moving target segmentation through low-rank constraints and group sparsity constraints. This method achieved good experimental results in dynamic background video sequences, but its implementation The process requires matrix decomposition and iterative operations, and the complexity is high

Kwak et al estimated the motion model satisfied by the front and background feature trajectories through non-parametric belief propagation, and completed the propagation of the model through Bayesian filtering. The area segmentation effect is not ideal

Images