Convex optimization method for three-dimensional (3D)-video-based time-space domain motion segmentation and estimation model

Abstract

The invention discloses a convex optimization method for a three-dimensional (3D)-video-based time-space domain motion segmentation and estimation model. The method is implemented by the following steps of: 1) establishing the 3D-video-based time-space domain motion segmentation and estimation model according to an active contour theory and a mapping relationship between a background three-dimensional motion parameter and a two-dimensional light stream; 2) converting the model into a corresponding horizontal set description equation, calculating a corresponding gradient descent equation, calculating an equivalent equation of the gradient descent equation, calculating an energy function corresponding to the equivalent equation, and performing convex relaxation on the energy function to obtain a convexly-optimized time-space domain motion segmentation and estimation model; and 3) introducing a cost variable into the further relaxation of the convexly-optimized time-space domain motion segmentation and estimation model, minimizing the convexly-optimized time-space domain motion segmentation and estimation model by adopting a multi-variable alternate iteration algorithm, and performing iterative convergence to obtain a final split surface according to a selected threshold function. The method has the advantages of high adaptability to changes in a target number, independence of a segmentation result on an initialized contour, and high operation efficiency.

Description

technical field [0001] The invention relates to the field of motion analysis based on computer vision, in particular to a method for transforming a motion segmentation and estimation model based on 3D video into a global convex optimization extremum problem by using the concept of convex relaxation. Background technique [0002] Motion analysis of video sequences is a basic research topic in the field of computer vision, and its application fields include public security monitoring, machine vision, automatic navigation, national defense weapons, digital media, video coding, 3D TV, virtual reality and intelligent transportation, etc. These are key development and research fields at home and abroad. [0003] Although it is important both in theory and in practice to perform motion segmentation and extract scenes from two-dimensional sequence images or videos to obtain three-dimensional structure and object motion information, due to the complexity of image formation, the diffe...

AI Technical Summary

Problems solved by technology

We have studied the multi-moving target segmentation, tracking, background motion compensation and motion speed estimation based on the level set method of partial differential equations. estimation, background motion compensation and segmentation of moving objects, but cannot estimate the 3D motion of the camera

However, the level set method also has certain limitations, that i

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