Relevant bibliographies by topics / Microsoft Kinect v1

Academic literature on the topic 'Microsoft Kinect v1'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Microsoft Kinect v1"

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Sokhib, Tukhtaev, and Taeg Keun Whangbo. "A Combined Method of Skin-and Depth-based Hand Gesture Recognition." International Arab Journal of Information Technology 17, no. 1 (January 1, 2019): 137–45. http://dx.doi.org/10.34028/iajit/17/1/16.

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Kinect is a promising acquisition device that provides useful information on a scene through color and depth data. There has been a keen interest in utilizing Kinect in many computer vision areas such as gesture recognition. Given the advantages that Kinect provides, hand gesture recognition can be deployed efficiently with minor drawbacks. This paper proposes a simple and yet efficient way of hand gesture recognition via segmenting a hand region from both color and depth data acquired by Kinect v1. The Inception model of the image recognition system is used to check the reliability of the proposed method. Experimental results are derived from a sample dataset of Microsoft Kinect hand acquisitions. Under the appropriate conditions, it is possible to achieve high accuracy in close to real time
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Temitayo, Ejidokun, Adigun Samson Olasunkanmi, Olutayo-Irheren E. Olutayo, and Rabbilfattah Ozovehe Yusuf. "Investigation into the suitability of kinect sensor for automated body measurement." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (February 1, 2023): 694. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp694-702.

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Due to the low cost and wide availability of the Kinect sensor, researchers and experts in the field of anthropometry, sizing and clothing fiting are leveraging on its inbuilt 3D camera to develop systems for automated body measurement. This study focuses on the evaluation of the Microsoft Kinect (V1) sensor to determine its suitability for automated body measurement. The study was conducted by data collection of various body dimensions of test subjects using a measuring tape as a reference. Furthermore, a statistical approach known as the measurement system analysis was used to investigate the sensor's capability to produce accurate, reliable and consistent body measurements. The results obtained shows indicates that there exists very little variation when the measurement is repeated. Also, the instrument is relatively stable, with minimal bias which can be corrected by calibration. The outcome of the study proves the effectiveness of the Microsoft Kinect sensor as a means of conducting body measurement.
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Pankov, B., and M. Makolkina. "OVERVIEW OF EQUIPMENT FOR CAPTURING 3D IMAGES WITH SUBSEQUENT TRANSMISSION THROUGH A COMMUNICATION NETWORK." Telecom IT 9, no. 3 (December 15, 2021): 56–71. http://dx.doi.org/10.31854/2307-1303-2021-9-3-56-71.

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The relatively recent advent of the publicly available consumer RGB-D sensors has enabled a wide range of functionality to interact with 3D technology. For example, devices such as Microsoft Kinect v1, Microsoft Kinect v2, and Intel Realsense F200 are currently available RGB-D sensors using Structured light and Time of Flight technologies for getting information about pixel depth. The article contains an extensive comparison of RGB-D devices in terms of the resolution of RGB and Depth cameras, latency (the time required to build a depth map and its processing), and also compares viewing angles, required USB interfaces, sensor sizes, etc. The main goal of the article is to provide a complete and visual comparison of devices that can potentially be used to capture three-dimensional images with their subsequent transmission by communication networks.
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Guidi, G., S. Gonizzi, and L. Micoli. "3D CAPTURING PERFORMANCES OF LOW-COST RANGE SENSORS FOR MASS-MARKET APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 33–40. http://dx.doi.org/10.5194/isprs-archives-xli-b5-33-2016.

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Since the advent of the first Kinect as motion controller device for the Microsoft XBOX platform (November 2010), several similar active and low-cost range sensing devices have been introduced on the mass-market for several purposes, including gesture based interfaces, 3D multimedia interaction, robot navigation, finger tracking, 3D body scanning for garment design and proximity sensors for automotive. However, given their capability to generate a real time stream of range images, these has been used in some projects also as general purpose range devices, with performances that for some applications might be satisfying. This paper shows the working principle of the various devices, analyzing them in terms of systematic errors and random errors for exploring the applicability of them in standard 3D capturing problems. Five actual devices have been tested featuring three different technologies: i) Kinect V1 by Microsoft, Structure Sensor by Occipital, and Xtion PRO by ASUS, all based on different implementations of the Primesense sensor; ii) F200 by Intel/Creative, implementing the Realsense pattern projection technology; Kinect V2 by Microsoft, equipped with the Canesta TOF Camera. A critical analysis of the results tries first of all to compare them, and secondarily to focus the range of applications for which such devices could actually work as a viable solution.
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Guidi, G., S. Gonizzi, and L. Micoli. "3D CAPTURING PERFORMANCES OF LOW-COST RANGE SENSORS FOR MASS-MARKET APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 33–40. http://dx.doi.org/10.5194/isprsarchives-xli-b5-33-2016.

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Since the advent of the first Kinect as motion controller device for the Microsoft XBOX platform (November 2010), several similar active and low-cost range sensing devices have been introduced on the mass-market for several purposes, including gesture based interfaces, 3D multimedia interaction, robot navigation, finger tracking, 3D body scanning for garment design and proximity sensors for automotive. However, given their capability to generate a real time stream of range images, these has been used in some projects also as general purpose range devices, with performances that for some applications might be satisfying. This paper shows the working principle of the various devices, analyzing them in terms of systematic errors and random errors for exploring the applicability of them in standard 3D capturing problems. Five actual devices have been tested featuring three different technologies: i) Kinect V1 by Microsoft, Structure Sensor by Occipital, and Xtion PRO by ASUS, all based on different implementations of the Primesense sensor; ii) F200 by Intel/Creative, implementing the Realsense pattern projection technology; Kinect V2 by Microsoft, equipped with the Canesta TOF Camera. A critical analysis of the results tries first of all to compare them, and secondarily to focus the range of applications for which such devices could actually work as a viable solution.
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Nichols, Andrew, Matteo Rubinato, Yun-Hang Cho, and Jiayi Wu. "Optimal Use of Titanium Dioxide Colourant to Enable Water Surfaces to Be Measured by Kinect Sensors." Sensors 20, no. 12 (June 21, 2020): 3507. http://dx.doi.org/10.3390/s20123507.

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Recent studies have sought to use Microsoft Kinect sensors to measure water surface shape in steady flows or transient flow processes. They have typically employed a white colourant, usually titanium dioxide (TiO2), in order to make the surface opaque and visible to the infrared-based sensors. However, the ability of Kinect Version 1 (KV1) and Kinect Version 2 (KV2) sensors to measure the deformation of ostensibly smooth reflective surfaces has never been compared, with most previous studies using a V1 sensor with no justification. Furthermore, the TiO2 has so far been used liberally and indeterminately, with no consideration as to the type of TiO2 to use, the optimal proportion to use or the effect it may have on the very fluid properties being measured. This paper examines the use of anatase TiO2 with two generations of the Microsoft Kinect sensor. Assessing their performance for an ideal flat surface, it is shown that surface data obtained using the V2 sensor is substantially more reliable. Further, the minimum quantity of colourant to enable reliable surface recognition is discovered (0.01% by mass). A stability test shows that the colourant has a strong tendency to settle over time, meaning the fluid must remain well mixed, having serious implications for studies with low Reynolds number or transient processes such as dam breaks. Furthermore, the effect of TiO2 concentration on fluid properties is examined. It is shown that previous studies using concentrations in excess of 1% may have significantly affected the viscosity and surface tension, and thus the surface behaviour being measured. It is therefore recommended that future studies employ the V2 sensor with an anatase TiO2 concentration of 0.01%, and that the effects of TiO2 on the fluid properties are properly quantified before any TiO2-Kinect-derived dataset can be of practical use, for example, in validation of numerical models or in physical models of hydrodynamic processes.
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Rahman, Md Wasiur, and Marina L. Gavrilova. "Human Identification Using Gait Skeletal Joint Distance Features." International Journal of Software Science and Computational Intelligence 9, no. 4 (October 2017): 19–33. http://dx.doi.org/10.4018/ijssci.2017100102.

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Gait not only defines the way a person walks, but also provides insights on an individual's daily routine, mental state or even cognitive function. The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently. In this article, authors develop a biometric security system using gait-based skeletal information obtained from Microsoft Kinect v1 sensor. The gait cycle is calculated by detecting the three consecutive local minima between the joint distance of left and right ankles. Authors have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle. After mean and variance features are extracted from the distance feature vector, the KNN algorithm is used for classification purpose. The classification accuracy of the authors' approach is 93.33%. Experimental results show that the proposed approach achieves better recognition accuracy then other state-of-the-art approaches. Incorporating gait biometric in a situation awareness system for identification of a mental state is one of the future directions of this research.
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Salau, Jennifer, Jan Henning Haas, Wolfgang Junge, and Georg Thaller. "Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification." Animals 11, no. 1 (December 29, 2020): 50. http://dx.doi.org/10.3390/ani11010050.

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Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.
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Conference papers on the topic "Microsoft Kinect v1"

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Motsch, Jean, Sirine Benammar, and Yves Bergeon. "Interior mapping of a building: A real-life experiment with Microsoft Kinect for Windows v1 and RGBD-SLAM." In 2017 International Conference on Military Technologies (ICMT). IEEE, 2017. http://dx.doi.org/10.1109/miltechs.2017.7988852.

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