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This paper describes a method to intelligently schedule a network of multiple RGBD sensors in a Bayesian object tracking scenario, with special focus on Microsoft KinectTM devices. These setups have issues such as the large amount of raw data generated by the sensors and interference caused by overlapping fields of view. The proposed algorithm addresses these issues by selecting and exclusively activating the sensor that yields the best measurement, as defined by a novel stochastic model that also considers hardware constraints and intrinsic parameters. In addition, as existing solutions to toggle the sensors were found to be insufficient, the development of a hardware module, especially designed for quick toggling and synchronization with the depth stream, is also discussed. The algorithm then is evaluated within the scope of a multi-Kinect object tracking scenario and compared to other scheduling strategies. |
