A Quality Evaluation of Single and Multiple Camera Calibration Approaches for an Indoor Multi Camera Tracking System
Other literature type
(issn: 2194-9034, eissn: 2194-9034)
Human detection and tracking has been a prominent research area for several scientists around the globe. State of the art algorithms
have been implemented, refined and accelerated to significantly improve the detection rate and eliminate false positives. While 2D approaches
are well investigated, 3D human detection and tracking is still an unexplored research field. In both 2D/3D cases, introducing
a multi camera system could vastly expand the accuracy and confidence of the tracking process. Within this work, a quality evaluation
is performed on a multi RGB-D camera indoor tracking system for examining how camera calibration and pose can affect the quality of
human tracks in the scene, independently from the detection and tracking approach used. After performing a calibration step on every
Kinect sensor, state of the art single camera pose estimators were evaluated for checking how good the quality of the poses is estimated
using planar objects such as an ordinate chessboard. With this information, a bundle block adjustment and ICP were performed for
verifying the accuracy of the single pose estimators in a multi camera configuration system. Results have shown that single camera
estimators provide high accuracy results of less than half a pixel forcing the bundle to converge after very few iterations. In relation
to ICP, relative information between cloud pairs is more or less preserved giving a low score of fitting between concatenated pairs.
Finally, sensor calibration proved to be an essential step for achieving maximum accuracy in the generated point clouds, and therefore
in the accuracy of the produced 3D trajectories, from each sensor.