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      Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect

      , ,
      Applied Sciences
      MDPI AG

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          Abstract

          The Azure Kinect, the successor of Kinect v1 and Kinect v2, is a depth sensor. In this paper we evaluate the skeleton tracking abilities of the new sensor, namely accuracy and precision (repeatability). Firstly, we state the technical features of all three sensors, since we want to put the new Azure Kinect in the context of its previous versions. Then, we present the experimental results of general accuracy and precision obtained by measuring a plate mounted to a robotic manipulator end effector which was moved along the depth axis of each sensor and compare them. In the second experiment, we mounted a human-sized figurine to the end effector and placed it in the same positions as the test plate. Positions were located 400 mm from each other. In each position, we measured relative accuracy and precision (repeatability) of the detected figurine body joints. We compared the results and concluded that the Azure Kinect surpasses its discontinued predecessors, both in accuracy and precision. It is a suitable sensor for human–robot interaction, body-motion analysis, and other gesture-based applications. Our analysis serves as a pilot study for future HMI (human–machine interaction) designs and applications using the new Kinect Azure and puts it in the context of its successful predecessors.

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          RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments

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            Robust Part-Based Hand Gesture Recognition Using Kinect Sensor

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              Efficient human pose estimation from single depth images.

              We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                June 2021
                June 21 2021
                : 11
                : 12
                : 5756
                Article
                10.3390/app11125756
                042ce054-0635-4da8-9819-9fc224e737d2
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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