Consistency Estimation Method of Distributed Information of Human Joints Based on Interactive Multi-model

Abstract

The invention discloses a method for estimating the consistency of distributed information of human joint points based on interactive multi-models, the position initialization of the joint points of the skeleton; the motion estimation of the joint points by local sensors: the construction of the motion model and the observation model of the joint points of the human body, and the realization of the state of the joint points Efficient estimation of the target joint points between sensors: each sensor sends its own estimated joint point information vector, information matrix and its corresponding information contribution, model probability to the adjacent communication sensor nodes, and Receive the information of the surrounding sensors, use the information consistency algorithm, fuse the estimation results of the surrounding sensors, and iterate several times in a row to achieve the convergence of the algorithm and the estimation results. By building a distributed RGBD sensor network and using the information consistency algorithm, the human body is realized Distributed estimation of joint points, no data fusion center in the network, improves the robustness of the system to node information error and invalidity, and makes it easier to expand the sensor network.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a method for estimating the consistency of distributed information of joint points of a human body based on interactive multi-models. Background technique [0002] Human behavior recognition based on multiple RGBD cameras has attracted extensive attention from researchers, and has been applied to human behavior detection in operating rooms, factory workshops, automobile assembly, indoor monitoring and other environments, effectively solving the problem of human occlusion and possible human- The robot collision problem has important application value. [0003] At present, human behavior perception based on multiple RGBD sensors is still in a centralized stage, requiring one or more data fusion centers to fuse 3D data and human skeleton joint point data, which requires high computing power and robustness for data fusion centers , weak resistance to network instability and low...

AI Technical Summary

Problems solved by technology

[0003] At present, human behavior perception based on multiple RGBD sensors is still in a centralized stage, requiring one or more data fusion centers to fuse 3D data and human skeleton joint point data, which requires high computing power and robustness for data fusion centers , Weak resistance to network instability and low scalability

[0004] Among them, the related technology for the consensus estimation of the distributed information of the human body joints with interactive multi-models has not yet appeared.

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