Evaluating and Improving the Depth Accuracy of Kinect for Windows v2

Yang, Lin; Zhang, Longyu; Dong, Haiwei; Alelaiwi, Abdulhameed; Saddik, Abdulmotaleb El

doi:10.1109/jsen.2015.2416651

Cited by 259 publications

(181 citation statements)

References 21 publications

(16 reference statements)

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“…When a person stands in front of the sensor, it captures color image and the depth image of the person using the RGB, IR and depth sensors. Then the background noise and moving objects are identified using image processing techniques in the sensor middleware [11]. The depth information of the user's body was identified according to the user position.…”

Section: Methodsmentioning

confidence: 99%

Non-Contact Human Body Parameter Measurement Based on Kinect Sensor

Adikari¹,

Ganegoda²,

Wanniarachchi³

2017

IOSR JCE

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Section: Methodsmentioning

confidence: 99%

Non-Contact Human Body Parameter Measurement Based on Kinect Sensor

Adikari¹,

Ganegoda²,

Wanniarachchi³

2017

IOSR JCE

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“…However, possible sensor limitations are related to inaccuracies regarding the coordinates of the captured 3D data, especially for short and long range distances (Yang et al, 2015). To address these difficulties, in this paper, we introduce a methodology which exploits the spatiotemporal coherency of a human movement in order to compensate the depth inaccuracies of the Kinect sensor.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Low-Cost Markerless Tracking System for Trajectory Interpretation

Laggis

Doulamis

Protopapadakis

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The tracking abilities of 1 st generation Kinect sensors have been tested over common trajectories of folk dances. Trajectories related errors, including offset, curve shape, noisy points are investigated and mitigated using well-known signal processing filters. Low cost depth trackers can contribute towards the remote tutoring of folk dances, by providing adequate data to instructors and explicit details to the trainees which segments of their dance trajectories need more work.

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“…In the article of Yang [16], an MS Kinect v2 structure is proposed to improve the accuracy of the sensors and the depth of capture of objects that are placed more than four meters apart. It has been concluded that an object covered with light-absorbing materials, may cause less reflected IR light back to the MS Kinect and therefore erroneous depth data.…”

Section: Accuracy Estimation Methodologies For 3d Vision Sensorsmentioning

confidence: 99%

A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors

Fernández¹,

Ávila²,

Gonçalves³

2017

Preprint

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Abstract:We propose an approach for estimating the error in depth data provided by generic 3D sensors, which are modern devices capable of generating an image (RGB data) and a depth map (distance) or other similar 2.5D structure (e.g. stereo disparity) of the scene. Our approach starts capturing images of a checkerboard pattern devised for the method. Then proceed with the construction of a dense depth map using functions that generally comes with the device SDK (based on disparity or depth). The 2D processing of RGB data is performed next to find the checkerboard corners. Clouds of corner points are finally created (in 3D), over which an RMS error estimation is computed. We come up with a multi-platform system and its verification and evaluation has been done, using the development kit of the board nVIDIA Jetson TK1 with the MS Kinects v1/v2 and the Stereolabs ZED camera. So the main contribution is the error determination procedure that does not need any data set or benchmark, thus relying only on data acquired on-the-fly. With a simple checkerboard, our approach is able to determine the error for any device. Envisioned application is on 3D reconstruction for robotic vision, with a series of 3D vision sensors embarked in robots (UAV of type quadcopter and terrestrial robots) for high-precision map construction, which can be used for sensing and monitoring.

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Evaluating and Improving the Depth Accuracy of Kinect for Windows v2

Cited by 259 publications

References 21 publications

Non-Contact Human Body Parameter Measurement Based on Kinect Sensor

Non-Contact Human Body Parameter Measurement Based on Kinect Sensor

A Low-Cost Markerless Tracking System for Trajectory Interpretation

A Generic Approach for Error Estimation of Depth Data from (Stereo and RGB-D) 3D Sensors

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