Automatic Extrinsic Multi-Sensor Network Calibration based on Time Series Matching

We propose an automatic calibration approach to determine the extrinsic (inter-sensor) calibration of a multi-sensor network for people tracking. A plan-view approach is used and pairwise overlapping detection areas of the distributed sensors are assumed. By exploiting intra-sensor tracks of an unknown number of tracking targets, we solve the referencing problem of the sensor fields of view by a matching of time series, avoiding any manual effort for the extrinsic calibration. We realize the automatic calibration exclusively based on intra-sensor tracking information by combining a trackwise w-RANSAC with a rotation-invariant distance measure, and an effective pre-filter method based on the walking speed of topological and temporal matched track pairs. Our automatic calibration routine is evaluated on a multi-sensor network, consisting of five depth sensors with a top-down view on an indoor scene, in which five people are randomly walking for approximately one minute. The track mapping accuracy of our automatic calibration method is compared to a calibration based on a manual selection of homologous image points. Therefore, we propose an evaluation method regarding the global track mapping accuracy. By excluding known track matches of our dataset from the calibration process, we derive an assumption about the global tracking performance of the calibrated multi-sensor network.