Human skeleton tracking system and method based on multiple kinects

Abstract

The present invention provides a system and method for tracking human body skeletons based on multiple Kinects, comprising: a first terminal comprising a plurality of Kinects, collecting human body images into point cloud data; a data transmission module; a second terminal comprising: a data receiving module, a receiving point Cloud data; the data conversion module unifies the camera coordinate system of multiple Kinects and the coordinate system of the shooting points to obtain the initial transformation relationship of each two Kinects; the data fusion module performs initial pairing of each two Kinects to obtain a Kinect A variety of pairing methods, iteratively obtain the best pairing of each Kinect through the least square method, and fuse the point cloud data of multiple Kinects according to the best transformation relationship of each two Kinects; the skeleton extraction module, according to the fusion of the data fusion module Human body reconstruction from point cloud data. The above-mentioned system and method reduce the misalignment phenomenon of point cloud data registration.

Description

technical field [0001] The present invention relates to the field of vision technology, and more specifically, to a multi-Kinect-based human skeleton tracking system and method. Background technique [0002] With the vigorous development of human-computer interaction technology, somatosensory devices have been widely used in shopping malls, entertainment, medical and other fields. A single Kinect has a small interactive range and limited accuracy. In order to meet the greater needs of actual users and researchers, existing somatosensory devices still have many deficiencies, such as: the collection distance of a single device is short, the range of human-computer interaction is small, the collection accuracy of the device itself, and Problems such as user self-occlusion and external noise. [0003] The current somatosensory devices mainly have problems such as limited interaction range, user self-occlusion and insufficient interaction accuracy. In order to solve these prob...

AI Technical Summary

Problems solved by technology

[0005] In order to increase the accuracy of the skeleton data in the existing multi-Kinect human-computer interaction technology, the geometric triangulation method is used to accurately place the specific positions and directions of the three Kinect cameras, so as to solve the problem of the lack of accuracy of a single Kinect and the For problems such as self-occlusion, the position and direction of each Kinect must be accurately placed in terms of physical geometry. Such a scene is difficult to build, and their position information must be determined again for reuse.

In addition, the existing multi-Kinect human-computer interaction technology has not dealt with the possible external interference and noise

Furthermore, even if the existing multi-Kinect human-computer interaction technology is placed accurately, there will still be a large number of dislocations in the registration of point cloud data, and the registration accuracy will be low.

Images