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Posture and Motion Detection Technologies: Stereo, Time Of Flight and Structured Light Sensors

Yıl 2018, Cilt: 11 Sayı: 1, 57 - 73, 31.01.2018
https://doi.org/10.17671/gazibtd.327215

Öz

This article is a
review study of the work done using posture and motion perceptions according to
their usage areas. Stereo, tof (time of flight), structural light sensors and
studies using depth data have been studied in detail. The focus of the work is
creating a Kinect device that detects movement and motion using structural
light. The advantages and weaknesses of the Kinect device compared to other
devices have been examined. The research has been divided into 4 classes as
education, robotics, health and others. In addition, a new study on Kinect is
suggested in the field of health by examining the methods used in the study.

Kaynakça

  • [1] Voltrium Systems. “Stereo Products”. https://voltrium.wordpress.com/machine-vision/home/stereo-products/ (16.01.2017).
  • [2] Boris and Patrick. “ZED stereo camera simulates human visual depth perception”. https://thenextweb.com/dd/2015/05/19/zed-stereo-camera-simulates-human-visual-depth-perception/#.tnw_6Jqb68n3 (16.01.2017).
  • [3] Hizook. “Low-Cost Depth Cameras (aka Ranging Cameras or RGB-D Cameras) to Emerge in 2010?”. http://www.hizook.com/blog/2010/03/28/low-cost-depth-cameras-aka-ranging-cameras-or-rgb-d-cameras-emerge-2010 (16.01.2017).
  • [4] Hizook. “Low-Cost Depth Cameras (aka Ranging Cameras or RGB-D Cameras) to Emerge in 2010?”. http://www.hizook.com/blog/2010/03/28/low-cost-depth-cameras-aka-ranging-cameras-or-rgb-d-cameras-emerge-2010 (16.01.2017).
  • [5] Engadget. “Wave goodbye to Microsoft’s original Kinect for Windows”. https://www.engadget.com/2014/12/31/oroginal-kinect-discontinued/ (16.01.2017).
  • [6] Michael Buckwald. “Leap Motion Controller”. https://store-us.leapmotion.com/products/leap-motion-controller (06.07.2017).
  • [7] Li L. "Time-of-flight camera–an introduction." Technical White Paper, 2014.
  • [8] Zagura. “How Does The Kinect 2 Compare to the Kinect 1?”. http://zugara.com/how-does-the-kinect-2-compare-to-the-kinect-1 (16.01.2017).
  • [9] Kora T, Soga M, Taki H. “Golf Learning Environment Enabling Overlaid Display of Expert's Model Motion and Learner's Motion Using KINECT”. Procedia Computer Science, 60, 1559-1565, 2015.
  • [10] Zarzuela MM, Pernas FJD, Calzón SM, Ortega DG, Rodríguez MA. “Educational Tourism through a Virtual Reality Platform”. Procedia Computer Science, 25, 382-388, 2013.
  • [11] Ayala NAR, Mendívil EG, Salinas P, Rios H. “Kinesthetic Learning Applied to Mathematics Using Kinect”. Procedia Computer Science, 25, 131-135, 2013.
  • [12] Munaro M, Ballin G, Michieletto S, Menegatti E. “3D flow estimation for human action recognition from colored point clouds”. Biologically Inspired Cognitive Architectures, 5, 42-51, 2013.
  • [13] Sanna A, Lamberti F, Paravati G, Manuri F. “A Kinect-based natural interface for quadrotor control”. Entertainment Computing, 4(3), 179-186, 2013.
  • [14] Stoyanov T, Mojtahedzadeh R, Andreasson H, Lilienthal AJ. “Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications”. Robotics and Autonomous Systems, 61(10), 1094-1105, 2013
  • [15] Sgorbissa A, Verda D. “Structure-based object representation and classification in mobile robotics through a Microsoft Kinect”. Robotics and Autonomous Systems, 61(12), 1665-1679, 2013.
  • [16] Du G, Zhang P. “Markerless human–robot interface for dual robot manipulators using Kinect sensor”. Robotics and Computer-Integrated Manufacturing, 30(2), 150-159, 2014.
  • [17] Rosado J, Silva F, Santos V. “Using Kinect for Robot Gesture Imitation”. Procedia Technology, 17, 423-430, 2014.
  • [18] Ukida H, Tanaka K. “Mobile robot operation by gesture recognition using continuous human motion”. 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Hangzhou, 28 Jul 2015.
  • [19] Cheng L, Sun Q, Su H, Cong Y, Zhao S. "Design and implementation of human-robot interactive demonstration system based on Kinect". 2012 24th Chinese Control and Decision Conference (CCDC), Taiyuan, May 2012.
  • [20] Tsai ZR. “Robust Kinect-based guidance and positioning of a multidirectional robot by Log-ab recognition”. Expert Systems with Applications, 41(4),1271-1282,2014.
  • [21] Chang YJ, Chen SF, Huang JD. “A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities”. Research in Developmental Disabilities, 32(6), 2566-2570, 2011.
  • [22] Ray SJ, Teizer J. “Real-time construction worker posture analysis for ergonomics training”. Advanced Engineering Informatics, 26(2), 439-455, 2012.
  • [23] Filipe V, Fernandes F, Fernandes H, Sousa A, Paredes H, Barroso J. “Blind Navigation Support System based on Microsoft Kinect”. Procedia Computer Science, 14, 94-101, 2012.
  • [24] Gonçalves N, Rodrigues JL, Costa S, Soares F. “Automatic Detection of Stereotypical Motor Movements”. Procedia Engineering, 47, 590-593, 2012.
  • [25] Dutta T. “Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace”. Applied Ergonomics, 43(4), 645-649, 2012.
  • [26] Clark RA, Pua YH, Fortin K, Ritchie C, Webster KE, Denehy L, Bryant AL. “Validity of the Microsoft Kinect for assessment of postural control”. Gait & Posture, 36(3), 372-377, 2012.
  • [27] O’Donovan C, Hirsch E, Holohan E, McBride I, McManus R, Hussey J. “Energy expended playing Xbox Kinect™ and Wii™ games: a preliminary study comparing single and multiplayer modes”. Physiotherapy, 98(3), 224-229, 2012.
  • [28] Chang YJ, Han WY, Tsai YC. “A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy”. Research in Developmental Disabilities, 34(11), 3654-3659, 2013.
  • [29] Semeraro F, Frisoli A, Loconsole C, Bannò F, Tammaro G, Imbriaco G, Marchetti L, Cerchiari EL. “Motion detection technology as a tool for cardiopulmonary resuscitation (CPR) quality training: A randomised crossover mannequin pilot study”. Resuscitation, 84(4), 501-507, 2013.
  • [30] Holmes H, Wood J, Jenkins S, Winship P, Lunt D, Bostock S, Hill K. “Xbox Kinect™ represents high intensity exercise for adults with cystic fibrosis”. Journal of Cystic Fibrosis, 12(6), 604-608, 2013.
  • [31] Sholukha V, Bonnechere B, Salvia P, Moiseev F, Rooze M,, Jan SVS. “Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems”. Journal of Biomechanics, 46(14), 2363-2371, 2013.
  • [32] Zannatha JMI, Tamayo AJM, Sánchez ADG, Delgado JEL, Cheu LER, Arévalo WAS. “Development of a system based on 3D vision, interactive virtual environments, ergonometric signals and a humanoid for stroke rehabilitation”. Computer Methods and Programs in Biomedicine, 112(2), 239-249, 2013.
  • [33] Ferreira M, Carreiro A, Damasceno A. “Gesture Analysis Algorithms”. Procedia Technology, 9, 1273-1281, 2013.
  • [34] Ortega DG, Pernas FJD, Zarzuela MM, Rodríguez MA. “A Kinect-based system for cognitive rehabilitation exercises monitoring”. Computer Methods and Programs in Biomedicine, 113(2), 620-631, 2014.
  • [35] Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. “Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease”. Gait & Posture, 39(4),1062-1068, 2014.
  • [36] Mellecker RR, McManus AA. “Active video games and physical activity recommendations: A comparison of the Gamercize Stepper, XBOX Kinect and XaviX J-Mat”. Journal of Science and Medicine in Sport, 17(3), 288-292, 2014.
  • [37] Pompeu JE, Arduini LA, Botelho AR, Fonseca MBF, Pompeu SMAA, Pasin CT, Deutsch JE. “Feasibility, safety and outcomes of playing Kinect Adventures!™ for people with Parkinson's disease: a pilot study”. Physiotherapy, 100(2), 162-168, 2014.
  • [38] Su CJ, Chiang CY, Huang JY. “Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic”. Applied Soft Computing, 22, 652-666, 2014.
  • [39] Diest M, Stegenga J, Wörtche HJ, Postema K, Verkerke GJ, Lamoth CJC. “Suitability of Kinect for measuring whole body movement patterns during exergaming”. Journal of Biomechanics, 47(12), 2925-2932, 2014.
  • [40] Huber ME, Seitz AL, Leeser M, Sternad D. “Validity and reliability of Kinect skeleton for measuring shoulder joint angles: a feasibility study”. Physiotherapy, 101(4), 389-393, 2015.
  • [41] Chang YJ, Chen SF, Chuang AF. “A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments”. Research in Developmental Disabilities, 32(6), 2064-2068, 2011.
  • [42] Schwarz LA, Mkhitaryan A, Mateus D, Navab N. “Human skeleton tracking from depth data using geodesic distances and optical flow”. Image and Vision Computing, 30(3) ,217-226, 2012.
  • [43] Tang Y, Sun Z, Tan T. “Slice representation of range data for head pose estimation”. Computer Vision and Image Understanding, 128, 8-35,2014.
  • [44] Budzan S, Kasprzyk J. “Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications”. Optics and Lasers in Engineering, 77, 230-240, 2016.
  • [45] Hayat M, Bennamoun M, El-Sallam AA. “An RGB–D based image set classification for robust face recognition from Kinect data”. Neurocomputing, 171, 889-900,2016.
  • [46] Chattopadhyay P, Roy A, Sural S, Mukhopadhyay J. “Pose Depth Volume extraction from RGB-D streams for frontal gait recognition”. Journal of Visual Communication and Image Representation, 25(1), 53-63, 2014.
  • [47] Sujono, Gunawan AAS. “Face Expression Detection on Kinect Using Active Appearance Model and Fuzzy Logic”. Procedia Computer Science, 59, 268-274, 2015.
  • [48] Sato K, Wu H, Chen Q. “High-speed and High-accuracy Scene Flow Estimation Using Kinect”. Procedia Computer Science, 22, 945-953, 2013.
  • [49] Jiang B, Zhao F, Liu X. "Observation-oriented silhouette-aware fast full body tracking with Kinect." Journal of Manufacturing Systems 33(1), 209-217, 2014.
  • [50] Dominio F, Donadeo M, Zanuttigh P. “Combining multiple depth-based descriptors for hand gesture recognition”. Pattern Recognition Letters, 50,101-111, 2014.
  • [51] Seer S, Brändle N, Ratti C. “Kinects and human kinetics: A new approach for studying pedestrian behavior”. Transportation Research Part C: Emerging Technologies, 48, 212-228, 2014.
  • [52] Ibañez R, Soria A, Teyseyre A, Campo M. “Easy gesture recognition for Kinect”. Advances in Engineering Software, 76, 171-180, 2014.
  • [53] Chen L, Wei H, Ferryman J. "A survey of human motion analysis using depth imagery". Pattern Recognition Letters, 34(15), 1995-2006, 2013.

Duruş ve Hareket Algılama Teknolojileri: Stereo, Time Of Flight ve Yapısal Işık Algılayıcılar

Yıl 2018, Cilt: 11 Sayı: 1, 57 - 73, 31.01.2018
https://doi.org/10.17671/gazibtd.327215

Öz

Bu makale, duruş ve hareket algılayıcıları kullanılarak yapılan
çalışmaların kullanım alanlarına göre incelendiği bir derleme çalışmasıdır. Duruş
ve hareket algılayıcılarında yaygın olarak kullanılan, stereo, tof(time of
flight-uçuş süresi) ve yapısal ışık algılayıcıları ile derinlik verisi
bilgisinin kullanıldığı çalışmalar ayrıntılı olarak incelenmiştir. Çalışmanın
odağı yapısal ışık kullanılarak duruş ve hareket algılayan Kinect cihazı
oluşturmaktadır. Kinect cihazının diğer cihazlara göre üstünlükleri ve
zayıflıkları incelenmiştir. Yapılan araştırmalar eğitim, robotik, sağlık ve
diğerleri olmak üzere 4 sınıfa ayrılmıştır. Ayrıca çalışmalarda kullanılan
yöntemler incelenerek sağlık alanında Kinect ile ilgili yeni bir çalışma
önerilmektedir.

Kaynakça

  • [1] Voltrium Systems. “Stereo Products”. https://voltrium.wordpress.com/machine-vision/home/stereo-products/ (16.01.2017).
  • [2] Boris and Patrick. “ZED stereo camera simulates human visual depth perception”. https://thenextweb.com/dd/2015/05/19/zed-stereo-camera-simulates-human-visual-depth-perception/#.tnw_6Jqb68n3 (16.01.2017).
  • [3] Hizook. “Low-Cost Depth Cameras (aka Ranging Cameras or RGB-D Cameras) to Emerge in 2010?”. http://www.hizook.com/blog/2010/03/28/low-cost-depth-cameras-aka-ranging-cameras-or-rgb-d-cameras-emerge-2010 (16.01.2017).
  • [4] Hizook. “Low-Cost Depth Cameras (aka Ranging Cameras or RGB-D Cameras) to Emerge in 2010?”. http://www.hizook.com/blog/2010/03/28/low-cost-depth-cameras-aka-ranging-cameras-or-rgb-d-cameras-emerge-2010 (16.01.2017).
  • [5] Engadget. “Wave goodbye to Microsoft’s original Kinect for Windows”. https://www.engadget.com/2014/12/31/oroginal-kinect-discontinued/ (16.01.2017).
  • [6] Michael Buckwald. “Leap Motion Controller”. https://store-us.leapmotion.com/products/leap-motion-controller (06.07.2017).
  • [7] Li L. "Time-of-flight camera–an introduction." Technical White Paper, 2014.
  • [8] Zagura. “How Does The Kinect 2 Compare to the Kinect 1?”. http://zugara.com/how-does-the-kinect-2-compare-to-the-kinect-1 (16.01.2017).
  • [9] Kora T, Soga M, Taki H. “Golf Learning Environment Enabling Overlaid Display of Expert's Model Motion and Learner's Motion Using KINECT”. Procedia Computer Science, 60, 1559-1565, 2015.
  • [10] Zarzuela MM, Pernas FJD, Calzón SM, Ortega DG, Rodríguez MA. “Educational Tourism through a Virtual Reality Platform”. Procedia Computer Science, 25, 382-388, 2013.
  • [11] Ayala NAR, Mendívil EG, Salinas P, Rios H. “Kinesthetic Learning Applied to Mathematics Using Kinect”. Procedia Computer Science, 25, 131-135, 2013.
  • [12] Munaro M, Ballin G, Michieletto S, Menegatti E. “3D flow estimation for human action recognition from colored point clouds”. Biologically Inspired Cognitive Architectures, 5, 42-51, 2013.
  • [13] Sanna A, Lamberti F, Paravati G, Manuri F. “A Kinect-based natural interface for quadrotor control”. Entertainment Computing, 4(3), 179-186, 2013.
  • [14] Stoyanov T, Mojtahedzadeh R, Andreasson H, Lilienthal AJ. “Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications”. Robotics and Autonomous Systems, 61(10), 1094-1105, 2013
  • [15] Sgorbissa A, Verda D. “Structure-based object representation and classification in mobile robotics through a Microsoft Kinect”. Robotics and Autonomous Systems, 61(12), 1665-1679, 2013.
  • [16] Du G, Zhang P. “Markerless human–robot interface for dual robot manipulators using Kinect sensor”. Robotics and Computer-Integrated Manufacturing, 30(2), 150-159, 2014.
  • [17] Rosado J, Silva F, Santos V. “Using Kinect for Robot Gesture Imitation”. Procedia Technology, 17, 423-430, 2014.
  • [18] Ukida H, Tanaka K. “Mobile robot operation by gesture recognition using continuous human motion”. 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Hangzhou, 28 Jul 2015.
  • [19] Cheng L, Sun Q, Su H, Cong Y, Zhao S. "Design and implementation of human-robot interactive demonstration system based on Kinect". 2012 24th Chinese Control and Decision Conference (CCDC), Taiyuan, May 2012.
  • [20] Tsai ZR. “Robust Kinect-based guidance and positioning of a multidirectional robot by Log-ab recognition”. Expert Systems with Applications, 41(4),1271-1282,2014.
  • [21] Chang YJ, Chen SF, Huang JD. “A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities”. Research in Developmental Disabilities, 32(6), 2566-2570, 2011.
  • [22] Ray SJ, Teizer J. “Real-time construction worker posture analysis for ergonomics training”. Advanced Engineering Informatics, 26(2), 439-455, 2012.
  • [23] Filipe V, Fernandes F, Fernandes H, Sousa A, Paredes H, Barroso J. “Blind Navigation Support System based on Microsoft Kinect”. Procedia Computer Science, 14, 94-101, 2012.
  • [24] Gonçalves N, Rodrigues JL, Costa S, Soares F. “Automatic Detection of Stereotypical Motor Movements”. Procedia Engineering, 47, 590-593, 2012.
  • [25] Dutta T. “Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace”. Applied Ergonomics, 43(4), 645-649, 2012.
  • [26] Clark RA, Pua YH, Fortin K, Ritchie C, Webster KE, Denehy L, Bryant AL. “Validity of the Microsoft Kinect for assessment of postural control”. Gait & Posture, 36(3), 372-377, 2012.
  • [27] O’Donovan C, Hirsch E, Holohan E, McBride I, McManus R, Hussey J. “Energy expended playing Xbox Kinect™ and Wii™ games: a preliminary study comparing single and multiplayer modes”. Physiotherapy, 98(3), 224-229, 2012.
  • [28] Chang YJ, Han WY, Tsai YC. “A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy”. Research in Developmental Disabilities, 34(11), 3654-3659, 2013.
  • [29] Semeraro F, Frisoli A, Loconsole C, Bannò F, Tammaro G, Imbriaco G, Marchetti L, Cerchiari EL. “Motion detection technology as a tool for cardiopulmonary resuscitation (CPR) quality training: A randomised crossover mannequin pilot study”. Resuscitation, 84(4), 501-507, 2013.
  • [30] Holmes H, Wood J, Jenkins S, Winship P, Lunt D, Bostock S, Hill K. “Xbox Kinect™ represents high intensity exercise for adults with cystic fibrosis”. Journal of Cystic Fibrosis, 12(6), 604-608, 2013.
  • [31] Sholukha V, Bonnechere B, Salvia P, Moiseev F, Rooze M,, Jan SVS. “Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems”. Journal of Biomechanics, 46(14), 2363-2371, 2013.
  • [32] Zannatha JMI, Tamayo AJM, Sánchez ADG, Delgado JEL, Cheu LER, Arévalo WAS. “Development of a system based on 3D vision, interactive virtual environments, ergonometric signals and a humanoid for stroke rehabilitation”. Computer Methods and Programs in Biomedicine, 112(2), 239-249, 2013.
  • [33] Ferreira M, Carreiro A, Damasceno A. “Gesture Analysis Algorithms”. Procedia Technology, 9, 1273-1281, 2013.
  • [34] Ortega DG, Pernas FJD, Zarzuela MM, Rodríguez MA. “A Kinect-based system for cognitive rehabilitation exercises monitoring”. Computer Methods and Programs in Biomedicine, 113(2), 620-631, 2014.
  • [35] Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. “Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease”. Gait & Posture, 39(4),1062-1068, 2014.
  • [36] Mellecker RR, McManus AA. “Active video games and physical activity recommendations: A comparison of the Gamercize Stepper, XBOX Kinect and XaviX J-Mat”. Journal of Science and Medicine in Sport, 17(3), 288-292, 2014.
  • [37] Pompeu JE, Arduini LA, Botelho AR, Fonseca MBF, Pompeu SMAA, Pasin CT, Deutsch JE. “Feasibility, safety and outcomes of playing Kinect Adventures!™ for people with Parkinson's disease: a pilot study”. Physiotherapy, 100(2), 162-168, 2014.
  • [38] Su CJ, Chiang CY, Huang JY. “Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic”. Applied Soft Computing, 22, 652-666, 2014.
  • [39] Diest M, Stegenga J, Wörtche HJ, Postema K, Verkerke GJ, Lamoth CJC. “Suitability of Kinect for measuring whole body movement patterns during exergaming”. Journal of Biomechanics, 47(12), 2925-2932, 2014.
  • [40] Huber ME, Seitz AL, Leeser M, Sternad D. “Validity and reliability of Kinect skeleton for measuring shoulder joint angles: a feasibility study”. Physiotherapy, 101(4), 389-393, 2015.
  • [41] Chang YJ, Chen SF, Chuang AF. “A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments”. Research in Developmental Disabilities, 32(6), 2064-2068, 2011.
  • [42] Schwarz LA, Mkhitaryan A, Mateus D, Navab N. “Human skeleton tracking from depth data using geodesic distances and optical flow”. Image and Vision Computing, 30(3) ,217-226, 2012.
  • [43] Tang Y, Sun Z, Tan T. “Slice representation of range data for head pose estimation”. Computer Vision and Image Understanding, 128, 8-35,2014.
  • [44] Budzan S, Kasprzyk J. “Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications”. Optics and Lasers in Engineering, 77, 230-240, 2016.
  • [45] Hayat M, Bennamoun M, El-Sallam AA. “An RGB–D based image set classification for robust face recognition from Kinect data”. Neurocomputing, 171, 889-900,2016.
  • [46] Chattopadhyay P, Roy A, Sural S, Mukhopadhyay J. “Pose Depth Volume extraction from RGB-D streams for frontal gait recognition”. Journal of Visual Communication and Image Representation, 25(1), 53-63, 2014.
  • [47] Sujono, Gunawan AAS. “Face Expression Detection on Kinect Using Active Appearance Model and Fuzzy Logic”. Procedia Computer Science, 59, 268-274, 2015.
  • [48] Sato K, Wu H, Chen Q. “High-speed and High-accuracy Scene Flow Estimation Using Kinect”. Procedia Computer Science, 22, 945-953, 2013.
  • [49] Jiang B, Zhao F, Liu X. "Observation-oriented silhouette-aware fast full body tracking with Kinect." Journal of Manufacturing Systems 33(1), 209-217, 2014.
  • [50] Dominio F, Donadeo M, Zanuttigh P. “Combining multiple depth-based descriptors for hand gesture recognition”. Pattern Recognition Letters, 50,101-111, 2014.
  • [51] Seer S, Brändle N, Ratti C. “Kinects and human kinetics: A new approach for studying pedestrian behavior”. Transportation Research Part C: Emerging Technologies, 48, 212-228, 2014.
  • [52] Ibañez R, Soria A, Teyseyre A, Campo M. “Easy gesture recognition for Kinect”. Advances in Engineering Software, 76, 171-180, 2014.
  • [53] Chen L, Wei H, Ferryman J. "A survey of human motion analysis using depth imagery". Pattern Recognition Letters, 34(15), 1995-2006, 2013.
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Fecir Duran

Alper Kaya Bu kişi benim

Yayımlanma Tarihi 31 Ocak 2018
Gönderilme Tarihi 7 Temmuz 2017
Yayımlandığı Sayı Yıl 2018 Cilt: 11 Sayı: 1

Kaynak Göster

APA Duran, F., & Kaya, A. (2018). Duruş ve Hareket Algılama Teknolojileri: Stereo, Time Of Flight ve Yapısal Işık Algılayıcılar. Bilişim Teknolojileri Dergisi, 11(1), 57-73. https://doi.org/10.17671/gazibtd.327215