Calibration of multiple kinects with little overlap regions

Nakazawa, Makoto; Mitsugami, Ikuhisa; Habe, Hitoshi; Yamazoe, Hirotake; Yagi, Yasushi

doi:10.1002/tee.22171

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…In addition, the information about the frontal orientation provided by our method will also help improve the robustness of the tracking. As the kinects asynchronously capture depth data, a temporal interpolation [60] would be required. Our future works aim at optimizing the method to reach a real-time performance in order to embedded efficiently in human tracking applications.…”

Section: Discussionmentioning

confidence: 99%

A new 3D descriptor for human classification: application for human detection in a multi-kinect system

Essmaeel¹,

Migniot²,

Dipandà³

et al. 2019

Multimed Tools Appl

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In this paper we present a new 3D descriptor for human classification and a human detection method based on this descriptor. The proposed 3D descriptor allows for the classification of an object represented by a point cloud, as human or non-human. It is derived from the well-known Histogram of Oriented Gradient by employing surface normals instead of gradients. The process consists in an appropriate subdivision of the object point cloud into blocks. These blocks provide the spatial distribution modeling of the surface normal orientation into the different parts of the object. This distribution modelling is expressed in the form of a histogram. In addition we have set up a multi-kinect acquisition system that provides us with Complete Point Clouds (CPC) (i.e. 360 • view). Such CPCs enable a suitable processing, particularly in case of occlusions. Moreover they allow for the determination of the human frontal orientation. Based on the proposed 3D descriptor, we have developed a human detection method that is applied on CPCs. First, we evaluated the 3D descriptor over a set of CPC candidates by using the Support Vector Machine (SVM) classifier. The learning process was conducted with the original CPC database that we have built. The results are very promising. The descriptor can discriminate human from non-human candidates and provides the frontal direction of the humans with high precision. In addition we demonstrated that using the CPCs improves significantly the classification results in comparison with Single Point Clouds (i.e. points clouds acquired with only K. Essmaeel, C.

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Section: Discussionmentioning

confidence: 99%

A new 3D descriptor for human classification: application for human detection in a multi-kinect system

Essmaeel¹,

Migniot²,

Dipandà³

et al. 2019

Multimed Tools Appl

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“…Calculate the mean and standard deviation of the deviation between the detection point and the true point,see (8)and (9). The results of Kalibr [34], OpenPTrack calibration results, and the deviation of the X-axis and Y-axis of the three stages of the method in this paper are counted, as shown in Tab.2.This paper proposes a complete system method to obtain a calibration error of 2cm, and the effect is greatly improved compared with Kalibr and OpenPTrack.…”

Section: Methodsmentioning

confidence: 99%

An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV

Yuesheng,

Tao,

Long

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras' extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.

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“…Besides, it also proposes a new depth error model of the Kinect. Nakazawa M [7] calibrates the Kinect in the case of using multiple Kinects and has used an extra RGB camera to obtain the correspondences between color images from the Kinects. And he optimizes the estimated parameters by minimizing both the errors of correspondences between color images and range data of planar regions.…”

Section: Introductionmentioning

confidence: 99%

The Calibration of Kinect Camera Based on ARtoolkit

Han¹,

Bai²,

Bai³

et al. 2017

dtetr

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Abstract. To deal with mutual occlusion of the virtual and real objects in the augmented reality scene, their depth values should be compared by using ARtoolkit and Kinect application programming interfaces (APIs). To improve the accuracy of the experiment, it has given up the existing default camera parameters file in ARtoolkit, and calibrated the Kinect camera to generate its own parameters data file. And different from the existing Kinect camera calibration methods, it abandoned the method of using the open NI SDK or extra high definition color camera as an intermediate bridge. Experimental results show this method has achieved a high accuracy and the validity by verifying in ARtoolkit.

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Calibration of multiple kinects with little overlap regions

Cited by 8 publications

References 17 publications

A new 3D descriptor for human classification: application for human detection in a multi-kinect system

A new 3D descriptor for human classification: application for human detection in a multi-kinect system

An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV

The Calibration of Kinect Camera Based on ARtoolkit

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