Abstract:
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 ...Show MoreMetadata
Abstract:
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.
Date of Conference: 08-11 July 2024
Date Added to IEEE Xplore: 11 October 2024
ISBN Information:
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- IEEE Keywords
- Index Terms
- Time Series ,
- Automatic Calibration ,
- Multi-sensor Networks ,
- Walking Speed ,
- Depth Camera ,
- Manual Selection ,
- Automatic Approach ,
- Manual Effort ,
- Tracking Information ,
- Calibration Approach ,
- Top-down View ,
- Global Tracking ,
- People Tracking ,
- Pairing ,
- Local Coordinate ,
- Sensor Networks ,
- Corresponding Points ,
- Pair Of Points ,
- Multiple Sensors ,
- Overlap Area ,
- Random Sample Consensus ,
- Local Tracking ,
- Pair Of Sensors ,
- Track Segment ,
- Local Coordinate System ,
- Temporal Overlap ,
- Thin-plate Spline ,
- Movement Vector ,
- Consensus Set ,
- Objects In The Scene
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Time Series ,
- Automatic Calibration ,
- Multi-sensor Networks ,
- Walking Speed ,
- Depth Camera ,
- Manual Selection ,
- Automatic Approach ,
- Manual Effort ,
- Tracking Information ,
- Calibration Approach ,
- Top-down View ,
- Global Tracking ,
- People Tracking ,
- Pairing ,
- Local Coordinate ,
- Sensor Networks ,
- Corresponding Points ,
- Pair Of Points ,
- Multiple Sensors ,
- Overlap Area ,
- Random Sample Consensus ,
- Local Tracking ,
- Pair Of Sensors ,
- Track Segment ,
- Local Coordinate System ,
- Temporal Overlap ,
- Thin-plate Spline ,
- Movement Vector ,
- Consensus Set ,
- Objects In The Scene
- Author Keywords