Auto-calibration of depth camera networks for people tracking

Korkalo, Otto; Tikkanen, Tommi; Kemppi, Paul; Honkamaa, Petri

doi:10.1007/s00138-019-01021-z

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…We applied the thin-plate-spline (TPS) mappings [ 21 ] for both transforming the observations to the global frame, and for directly compensating the errors in the plan-view domain. The details of the approach are presented in [ 19 ], however, we summarize the main points here. The TPS mappings have been widely used in many computer vision applications, such as point set matching and image warping, and they have also been applied for modelling depth camera distortions [ 22 ].…”

Section: People Tracking With Depth Sensor Networkmentioning

confidence: 99%

“…The reference points can be collected, e.g., with a self-navigating mobile robot, which knows its exact location at every time step. To avoid manual tasks in the calibration process, we presented an autocalibration method in [ 19 ]. The goal of autocalibrating is two-fold: first, we determine the optimal structure of the reference points, and second, using the reference points, we can compute the TPS mappings for each camera, as well as the measurement noise models as described in Section 3 .…”

Section: People Tracking With Depth Sensor Networkmentioning

confidence: 99%

“… The noise model can be directly derived from people detections, and no separate calibration routines or calibration targets (such as checkerboard planes) are needed. When used in conjunction with our depth camera network self-calibration routine [ 19 ], it allows fully automatic system configuration. …”

Section: Introductionmentioning

confidence: 99%

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Measurement Noise Model for Depth Camera-Based People Tracking

Korkalo

Takala

2021

Sensors

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Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement noise model for depth camera-based people tracking. In our tracking solution, we utilise the so-called plan-view approach, where the 3D measurements are transformed to the floor plane, and the tracking problem is solved in 2D. We directly model the measurement noise in the plan-view domain, and the errors that originate from the imaging process and the geometric transformations of the 3D data are combined. We also present a method for directly defining the noise models from the observations. Together with our depth sensor network self-calibration routine, the approach allows fast and practical deployment of depth-based people tracking systems.

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Section: People Tracking With Depth Sensor Networkmentioning

confidence: 99%

Section: People Tracking With Depth Sensor Networkmentioning

confidence: 99%

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Measurement Noise Model for Depth Camera-Based People Tracking

Korkalo

Takala

2021

Sensors

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“…Due to the structure of the cab, the camera is installed exactly above the driver in order to not block the driver's view. While installing the camera, images with known size are shot to calibrate the camera so as to confirm the location of the original point of image coordinates in the actual coordinate system [5].…”

Section: Calculation Of Practical Location Of Eye Centermentioning

confidence: 99%

Intelligent Regulation Algorithm of Automobile Rear View Mirror based on Eye Location

Wu¹,

Xu²,

Dianshuang³

et al. 2019

Int J Performability Eng

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For the defects in the automobile rear view mirror regulation method, an intelligent regulation algorithm of automobile rear view mirrors is studied in this paper. The algorithm utilizes drivers' images in vehicles gathered by a single camera to determine the practical spatial positions of drivers' binocular centers in the vehicles. Then, through the regulation relationship algorithm between eye location and the rear view mirror, the intelligent regulation of optimal view of automobile rear view mirrors is achieved. The experimental results indicate that the algorithm has the advantages of fast regulation, convenience, and accuracy.

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“…In addition, scholars have proposed many improved methods for the calibration of depth cameras [20]. The functionality of popular depth cameras on the market is mainly accomplished through infrared imaging and projectors, hence the depth image is “carried” by the infrared image [21].…”

Section: Introductionmentioning

confidence: 99%

Calibration of the Relative Orientation between Multiple Depth Cameras Based on a Three-Dimensional Target

Meng

Gao

et al. 2019

Sensors

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Depth cameras play a vital role in three-dimensional (3D) shape reconstruction, machine vision, augmented/virtual reality and other visual information-related fields. However, a single depth camera cannot obtain complete information about an object by itself due to the limitation of the camera’s field of view. Multiple depth cameras can solve this problem by acquiring depth information from different viewpoints. In order to do so, they need to be calibrated to be able to accurately obtain the complete 3D information. However, traditional chessboard-based planar targets are not well suited for calibrating the relative orientations between multiple depth cameras, because the coordinates of different depth cameras need to be unified into a single coordinate system, and the multiple camera systems with a specific angle have a very small overlapping field of view. In this paper, we propose a 3D target-based multiple depth camera calibration method. Each plane of the 3D target is used to calibrate an independent depth camera. All planes of the 3D target are unified into a single coordinate system, which means the feature points on the calibration plane are also in one unified coordinate system. Using this 3D target, multiple depth cameras can be calibrated simultaneously. In this paper, a method of precise calibration using lidar is proposed. This method is not only applicable to the 3D target designed for the purposes of this paper, but it can also be applied to all 3D calibration objects consisting of planar chessboards. This method can significantly reduce the calibration error compared with traditional camera calibration methods. In addition, in order to reduce the influence of the infrared transmitter of the depth camera and improve its calibration accuracy, the calibration process of the depth camera is optimized. A series of calibration experiments were carried out, and the experimental results demonstrated the reliability and effectiveness of the proposed method.

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Auto-calibration of depth camera networks for people tracking

Cited by 5 publications

References 25 publications

Measurement Noise Model for Depth Camera-Based People Tracking

Measurement Noise Model for Depth Camera-Based People Tracking

Intelligent Regulation Algorithm of Automobile Rear View Mirror based on Eye Location

Calibration of the Relative Orientation between Multiple Depth Cameras Based on a Three-Dimensional Target

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