Indoor 3D Video Monitoring Using Multiple Kinect Depth-Cameras
This work addresses indoor monitoring for applications like surveillance or activity analysis, but it is incremental as it builds on existing Kinect and compression techniques.
The authors tackled the problem of remote indoor 3D monitoring using multiple Kinect sensors by developing a client-server system that compresses 3D data into RGB images for network transmission and merges skeleton data to handle occlusions, resulting in robust people monitoring with labeled skeletons and joint trajectories for analysis.
This article describes the design and development of a system for remote indoor 3D monitoring using an undetermined number of Microsoft(R) Kinect sensors. In the proposed client-server system, the Kinect cameras can be connected to different computers, addressing this way the hardware limitation of one sensor per USB controller. The reason behind this limitation is the high bandwidth needed by the sensor, which becomes also an issue for the distributed system TCP/IP communications. Since traffic volume is too high, 3D data has to be compressed before it can be sent over the network. The solution consists in selfcoding the Kinect data into RGB images and then using a standard multimedia codec to compress color maps. Information from different sources is collected into a central client computer, where point clouds are transformed to reconstruct the scene in 3D. An algorithm is proposed to merge the skeletons detected locally by each Kinect conveniently, so that monitoring of people is robust to self and inter-user occlusions. Final skeletons are labeled and trajectories of every joint can be saved for event reconstruction or further analysis.