Распознавание жестов рук для взаимодействия человека с компьютером
[Hand Gesture Recognition for Human Computer Interaction]. 7-ая международная конференция по развитию в вычислениях и коммуникациях (2017) [7th International Conference on Advances in Computing & Communications, ICACC-2017, 22] Индия, Кочин. 24 августа 2017 года
Keywords:
человеко-машинный интерфейс, распознавание жестов, контуры, выпуклости, каскад ХаараAbstract
Использование физического контроллера, такого как мышь, клавиатура для взаимодействия человека с компьютером препятствует естественному интерфейсу, поскольку существует сильный барьер между пользователем и компьютером. В этой статье мы разработали надёжную систему распознавания жестов без маркеров, которая может эффективно отслеживать как статические, так и динамические жесты. Наша система переводит распознаваемый жест в такие действия, как открытие веб-сайтов и запуск приложений, таких как VLC Player и PowerPoint. Динамический жест используется для пролистывания слайдов в презентации. Наши результаты показывают, что интуитивное HCI (Human-computer interaction, человеко-машинное взаимодействие) может быть достигнуто с минимальными требованиями к оборудованию.
Экспертиза проведена под руководством научного комитета 7 й Международной конференции по достижениям в области вычислительной техники и связи.
Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications.
References
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7. Dulayatrakul J, Prasertsakul P, Kondo T, Nilkhamhang I. Robust implementation of hand gesture recognition for remote human-machine interaction. In: Information Technology and Electrical Engineering (ICITEE); 2015 7th International Conference on;2015 Oct 29; p. 247252.
8. Tsai TH, Huang CC, Zhang KL. Embedded virtual mouse system by using hand gesture recognition. In: Consumer Electronics-Taiwan (ICCE-TW); 2015 IEEE International Conference on; 2015 Jun 6; Taiwan, Taipei; New York: IEEE; 2015; p. 352-353.
9. Hussain I, Talukdar AK, Sarma KK. Hand gesture recognition system with real-time palm tracking. In: India Conference (INDICON);2014 Annual IEEE;2014 Dec 11; India, Pune; New York: IEEE; 2014; p. 1-6.
10. Huong TN, Huu TV, Le Xuan T. Static hand gesture recognition for vietnamese sign language (VSL) using principle components analysis. In: Communications, Management and Telecommunications (ComManTel); 2015 International Conference on; 2015 Dec 28; p. 138-141.
11. Chen Y, Luo B, Chen YL, Liang G, Wu X. A real-time dynamic hand gesture recognition system using kinect sensor. In: Robotics and Biomimetics (ROBIO); 2015 IEEE International Conference on; 2015 Dec 6; New York: IEEE;2015; p. 2026-2030.
12. C. Wang, Z. Liu and S. C. Chan. Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera. IEEE Transactions on Multimedia 2015; 17(1): 29-39.
13. Chen WL, Wu CH, Lin CH. Depth-based hand gesture recognition using hand movements and defects. In: Next-Generation Electronics (ISNE); 2015 International Symposium on; 2015 May 4; Taiwan, Taipei; New York: IEEE;2015; p. 1-4.
14. Wong WS, Hsu SC, Huang CL. Virtual touchpad: Hand gesture recognition for smartphone with depth camera. In: Consumer ElectronicsTaiwan (ICCE-TW);2015 IEEE International Conference on; 2015 Jun 6; Taiwan, Taipei; New York: IEEE;2015; p. 214-215.
15. Ishiyama H, Kurabayashi S. Monochrome glove: A robust real-time hand gesture recognition method by using a fabric glove with design of structured markers. In: Virtual Reality (VR); 2016 IEEE;2016 Mar 19; Greenville, SC; New York: IEEE;2016; p. 187-188.
16. Suriya R, Vijayachamundeeswari V. A survey on hand gesture recognition for simple mouse control. In: Information Communication and Embedded Systems (ICICES); 2014 International Conference on; 2014 Feb 27; India, Chennai; New York: IEEE;2014; p. 1-5.
17. Chanda K, Ahmed W, Mitra S. A new hand gesture recognition scheme for similarity measurement in a vision based barehanded approach. In: Image Information Processing (ICIIP); 2015 Third International Conference on; 2015 Dec 21;; New York: IEEE;2015; pp. 17-22.
18. Luzhnica G, Simon J, Lex E, Pammer V. A sliding window approach to natural hand gesture recognition using a custom data glove. In:3D User Interfaces (3DUI); 2016 IEEE Symposium on; 2016 Mar 19; Greenville, SC; New York: IEEE;2016; p. 81-90.
19. Chen Y, Ding Z, Chen YL, Wu X. Rapid recognition of dynamic hand gestures using leap motion. In: Information and Automation; 2015 IEEE International Conference on; 2015 Aug 8; New York: IEEE;2015; p. 1419-1424.
20. Otsu, Nobuyuki. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 1979; 9(1): 62-66.
2. Ji-Hwan Kim, Nguyen Duc Thang, Tae-Seong Kim. 3-D hand Motion Tracking and Gesture Recognition Using a Data Glove. In; Industrial Electronics; 2009 IEEE International Symposium on; 2009 July 5; New York: IEEE;2009; p.1013-1018.
3. Hung CH, Bai YW, Wu HY. Home outlet and LED array lamp controlled by a smartphone with a hand gesture recognition. In: Consumer Electronics (ICCE); 2016 IEEE International Conference on; 2016 Jan 7; New York: IEEE;2016; p.5-6.
4. Hung CH, Bai YW, Wu HY. Home appliance control by a hand gesture recognition belt in LED array lamp case. In: Consumer Electronics (GCCE); 2015 IEEE 4th Global Conference on; 2015 Oct 27; New York: IEEE;2015; p. 599-600
5. She Y, Wang Q, Jia Y, Gu T, He Q, Yang B. A real-time hand gesture recognition approach based on motion features of feature points. In: Computational Science and Engineering (CSE); 2014 IEEE 17th International Conference on; 2014 Dec 19; New York: IEEE;2014; p.10961102.
6. Lee DH, Hong KS. A Hand gesture recognition system based on difference image entropy. In: Advanced Information Management and Service (IMS), 2010 6th International Conference on; 2010 Nov 30; Seoul; New York: IEEE; 2010; p. 410-413.
7. Dulayatrakul J, Prasertsakul P, Kondo T, Nilkhamhang I. Robust implementation of hand gesture recognition for remote human-machine interaction. In: Information Technology and Electrical Engineering (ICITEE); 2015 7th International Conference on;2015 Oct 29; p. 247252.
8. Tsai TH, Huang CC, Zhang KL. Embedded virtual mouse system by using hand gesture recognition. In: Consumer Electronics-Taiwan (ICCE-TW); 2015 IEEE International Conference on; 2015 Jun 6; Taiwan, Taipei; New York: IEEE; 2015; p. 352-353.
9. Hussain I, Talukdar AK, Sarma KK. Hand gesture recognition system with real-time palm tracking. In: India Conference (INDICON);2014 Annual IEEE;2014 Dec 11; India, Pune; New York: IEEE; 2014; p. 1-6.
10. Huong TN, Huu TV, Le Xuan T. Static hand gesture recognition for vietnamese sign language (VSL) using principle components analysis. In: Communications, Management and Telecommunications (ComManTel); 2015 International Conference on; 2015 Dec 28; p. 138-141.
11. Chen Y, Luo B, Chen YL, Liang G, Wu X. A real-time dynamic hand gesture recognition system using kinect sensor. In: Robotics and Biomimetics (ROBIO); 2015 IEEE International Conference on; 2015 Dec 6; New York: IEEE;2015; p. 2026-2030.
12. C. Wang, Z. Liu and S. C. Chan. Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera. IEEE Transactions on Multimedia 2015; 17(1): 29-39.
13. Chen WL, Wu CH, Lin CH. Depth-based hand gesture recognition using hand movements and defects. In: Next-Generation Electronics (ISNE); 2015 International Symposium on; 2015 May 4; Taiwan, Taipei; New York: IEEE;2015; p. 1-4.
14. Wong WS, Hsu SC, Huang CL. Virtual touchpad: Hand gesture recognition for smartphone with depth camera. In: Consumer ElectronicsTaiwan (ICCE-TW);2015 IEEE International Conference on; 2015 Jun 6; Taiwan, Taipei; New York: IEEE;2015; p. 214-215.
15. Ishiyama H, Kurabayashi S. Monochrome glove: A robust real-time hand gesture recognition method by using a fabric glove with design of structured markers. In: Virtual Reality (VR); 2016 IEEE;2016 Mar 19; Greenville, SC; New York: IEEE;2016; p. 187-188.
16. Suriya R, Vijayachamundeeswari V. A survey on hand gesture recognition for simple mouse control. In: Information Communication and Embedded Systems (ICICES); 2014 International Conference on; 2014 Feb 27; India, Chennai; New York: IEEE;2014; p. 1-5.
17. Chanda K, Ahmed W, Mitra S. A new hand gesture recognition scheme for similarity measurement in a vision based barehanded approach. In: Image Information Processing (ICIIP); 2015 Third International Conference on; 2015 Dec 21;; New York: IEEE;2015; pp. 17-22.
18. Luzhnica G, Simon J, Lex E, Pammer V. A sliding window approach to natural hand gesture recognition using a custom data glove. In:3D User Interfaces (3DUI); 2016 IEEE Symposium on; 2016 Mar 19; Greenville, SC; New York: IEEE;2016; p. 81-90.
19. Chen Y, Ding Z, Chen YL, Wu X. Rapid recognition of dynamic hand gestures using leap motion. In: Information and Automation; 2015 IEEE International Conference on; 2015 Aug 8; New York: IEEE;2015; p. 1419-1424.
20. Otsu, Nobuyuki. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 1979; 9(1): 62-66.
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Published
2019-07-07
How to Cite
Elsevier B. Распознавание жестов рук для взаимодействия человека с компьютером: [Hand Gesture Recognition for Human Computer Interaction]. 7-ая международная конференция по развитию в вычислениях и коммуникациях (2017) [7th International Conference on Advances in Computing & Communications, ICACC-2017, 22] Индия, Кочин. 24 августа 2017 года / B. Elsevier, М.Р. Шалелашвили // Scientific works of the Central Research Institute of Russian Sign Language. – 2019. – № 2. – P. 195–212.
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Автор передаёт сроком на 5 лет ЦНИИ русского жестового языка неисключительные права на использование статьи в любой форме и любым способом, указанным в статье 1270 ГК РФ. Передача прав происходит в момент загрузки любых материалов через автоматизированную систему на данном сайте.
