In video based cross-model analysis of planning meeting, the meeting events are recorded by multiple cameras distributed in the entire meeting room. Subject’s hand gestures, hand motion, head orientations, gaze targets, body poses are very important for the meeting event analysis. In order to register everything to the same global coordinate system, build 3D model, get 3D data from the video, we need to create a proper meeting room configuration and calibrate all cameras to obtain their intrinsic and extrinsic parameters. However, the calibration of multiple cameras distributed in the entire meeting room area is a challenging task because it is impossible to let all cameras in the meeting room see a reference object at the same time and wide field-of-view cameras suffer under radial distortion. In this paper, we propose a simple approach to create a good meeting room configuration and calibrate multiple cameras in the meeting room. The proposed approach includes several steps. First, we create stereo camera pairs according to the room configuration and the requirements of the targets, the participants of the meeting. Second, we apply Tsai’s algorithm to calibrate each stereo camera pair and obtain the parameters in its own local coordinate system. Third, we use Vicon motion capture data to transfer all local coordinate systems of stereo camera pairs into a global coordinate system in the meeting room. We can obtain the positions, orientations, and parameters for all cameras in the same global coordinate system, so that we can register everything into this global coordinate system. Next, we do calibration error analysis for the current camera and meeting room configuration. We can obtain error distribution in the entire meeting room area. Finally, we improve the current camera and meeting room configuration according to the error dis
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