1. Turan B, Eskikurt Hİ, Can MS. An aplication based on artificial
neural network for determining viewpoint coordinates on a
screen. Elektronika Ir Elektrotechnika 22 2 (2016) 86-91.
DOI: http://dx.doi.org/10.5755/j01.eie.22.2.7586
2. Lee WPO, Kaoli C, Huang JY. A smart TV system with
body-gesture control, tag-based rating and context-aware
recommendation. Knowledge-Based Systems 56 (2014)
167-178. https://doi.org/10.1016/j.knosys.2013.11.007
3. Bellmore C, Ptucha R, Savakis A. Interactıve dısplay using
depth and RGB sensors for face and gesture control. Western
New York Image Processing Workshop, 2011. https://doi.
org/10.1109/WNYIPW.2011.6122883.
4. Yavşan E, Uçar A. Gesture imitation and recognition using
Kinect sensor and extreme learning machines. Measurement
94 (2016) 852–861.
5. Rahman ASMM, Saboune J, Saddik AE. Motion-path
based in car gesture control of the multimedia devices.
DIVANet '11 Proceedings of the first ACM international
symposium on Design and analysis of intelligent vehicular
networks and applications 69-76, 2016. https://doi.
org/10.1145/2069000.2069013.
6. Bhuiyan M, Picking R. Gesture-controlled user interfaces,
what have we done and what’s next?. 2009. http://citeseerx.
ist.psu.edu/viewdoc/download?doi=10.1.1.562.6140&rep
=rep1&type=pdf. (accessed 13 11 2018).
7. Kela J, Korpipaa P, Mantyjarvi J, Kallio S, Savino G, Jozzo L,
Marca S. Accelerometer-based gesture control for a desing
environment. Personal and Ubiquitous Computing (2006)
285-299. https://doi.org/10.1007/s00779-005-0033-
8. Mantyjarvi J, Kela J, Korpipaa P, Kallio S. Enabling fast and
effortless customisation in accelerometer based gestureinteraction. MUM '04 Proceedings of the 3rd international
conference on Mobile and ubiquitous multimedia 25-31,
2004. https://doi.org/10.1145/1052380.1052385
9. Hackenberg G, McCall R, Broll W. Lightweight Palm and
Finger Tracking for Real-Time 3D Gesture Control. 2011
IEEE Virtual Reality Conference, 2011. https://doi.
org/10.1109/VR.2011.5759431.
10. Akyol S, Canzler U, Bengler K, Hahn W. Gesture control
for use in automobiles. IAPR Workshop on Machine Vision
Applications, Nov. 28-30, 2000. The University of Tokyo,
Japan. https://pdfs.semanticscholar.org/fb51/6222c7c
87f42872a28ff8fc74139447b1280.pdf. (accessed 13 11
2018).
11. Bizzotto N, Costanza A, Bizzotto L, Revis D, Sandri A,
Mangan B. Leap motion gesture control with osirix in the
operating room to control imaging. Surgical Innovation,
2014. https://doi.org/10.1177/1553350614528384.
12. Cohen CJ, Beach G, Foulk G. A Basic Hand Gesture Control
System for PC Applications. IEEE Xplore Digital Library,
2001. https://doi.org/10.1109/AIPR.2001.991206.
13. Gallo L, Placitelli AP, Ciampi M. Controller-free exploration
of medical image data: Experiencing the Kinect. 2011
24th International Symposium on Computer-Based
Medical Systems (CBMS), 2011. https://doi.org/10.1109/
CBMS.2011.5999138.
14. Doğan RÖ, Doğan H, Köse C. Virtual Mouse Control with Hand
Gesture Information Extraction and Tracking. 23nd Signal
Processing and Communications Applications Conference
(SIU) 2015. https://ieeexplore.ieee.org/stamp/stamp.
jsp?arnumber=7130228. (accessed 10 12 2019).
15. Kaura HK, Honrao V, Patil S, Shetty P. Gesture Controlled
Robot using Image Processing. (IJARAI) International
Journal of Advanced Research in Artificial Intelligence, Vol.
2, No. 5, 201, 2013 https://pdfs.semanticscholar.org/
ff0f/20e3dbbdf257ec3ca36be4ed251036b49e11.pdf.
(accessed 13 11 2018).
16. Chowdary PRV, Babu MN, Subbareddy TV, Reddy BM,
Elamaran V. Image Processing Algorithms for Gesture
Recognition using MATLAB. 2014 IEEE International
Conference on Advanced Communication Control and
Computing Technologies (ICACCCT), 2014. https://doi.
org/10.1109/ICACCCT.2014.7019356.
17. Kar S, Banerjee S, Jana A, Kundu D, Chatterjee D, Ghosh
S, Mitra D, Gupta SD. Image Processing Based Customized
Image Editor and Gesture Controlled Embedded Robot
Coupled with Voice Control Features. (IJACSA) International
Journal of Advanced Computer Science and Applications
6 (2015) 11. https://pdfs.semanticscholar.org/f000/
be3e91d69dc8ce1f87ae32ae7e5395b09b86.pdf.
(accessed 13 11 2018).
18. Osimani C, Piedra-Fernandez JA, Ojeda-Castelo JJ, Iribarne
L. Hand Posture Recognition with Standard Webcam for
Natural Interaction, WorldCIST 2017. Advances in Intelligent
Systems and Computing, vol 570 (2017) Springer.
19. Hsiang-Yueh L, Hao-Yuan K, Yu-Chun H. Real-time Hand
Gesture Recognition System and Application. Sensors and
Materials, Vol. 30, No. 4 (2018) 869–884.
20. Schacher JC. Gesture control is sounds in 3D space.
Proceedings of the 2007 Conference on New Interfaces for
Musical Expression (NIME07), New York, NY, USA, 2007.
http://www.nime.org/proceedings/2007/nime2007_358.
pdf. (accessed 13 11 2018).
21. Erden F. Hand gesture recognition using two differential
PIR sensors and a camera. 2014 22nd Signal Processing
and Communications Applications Conference (SIU), 2014.
https://doi.org/10.1109/SIU.2014.6830237.
22. Şahin A. Hacking the Gestures of Past for Future
Interactions. M.Sc. THESIS, 2013 http://muep.mau.se/
bitstream/handle/2043/15700/Atilim%20Sahin%20-%20
Hacking%20the%20Gestures%20of%20Past%20for%20
Future%20Interactions.pdf?sequence=2&isAllowed=y.
(accessed 13 11 2018).
23. Rautaray S, Agrawal A. Vision based hand gesture
recognition for human computer interaction: a survey. Artif.
Intell. Rev. 43 (2015) 1–54
24. Munir O, Ali AN, Javaan C. Hand Gesture Recognition Based
on Computer Vision: A Review of Techniques. Journal of
Imaging 6(8) 2020 73.
26. Optical flow-based gesture motion direction recognition
method, https://patents.google.com/patent/
CN104331151A/en
27. Pathak B, Jalal AS. Motion Direction Code—A Novel Feature
for Hand Gesture Recognition. Advances in Intelligent
Systems and Computing, vol 798 2019. Springer
Realization of Gesture Control Application on Openmv Board Using Optical Flow in Real-Time Video Images
Year 2021,
Volume: 8 Issue: 2, 87 - 96, 30.06.2021
OpenMV Board is designed for purpose of non-complex image processing applications. It is an image processing sensor that has been a MicroPython embedded operating-system(OS).
In the study, it is aimed to develop gesture control applications for electrical household appliances and small budget devices. Therefore, the hardware to be used should be cheap and the algorithm should be simple. Thus three gesture control applications have been developed by using OpenMv board for use in different electrical appliances. These are 1-level control, 2-multi-component simple system control and 3-page flip. The algorithmsused in the study are independent of the user because they are optical flow-based. Thus,the use of low-cost simple gesture control applications for industrial purposes (electricalappliances) can be realized.
Algorithms developed for applications were written on the OpenMV IDE. These application results were monitored in real-time through the IDE. In addition, the algorithm developed for level control has been embedded and tested on an SD card on OpenMv independent of OpenMV IDE. During the test, output information was generated using OpenMV pins and the level indicator created using yellow, green and red LEDs connected to the pins was checked real-time. Thus, the algorithm was tested on a computer-independent embedded system.
1. Turan B, Eskikurt Hİ, Can MS. An aplication based on artificial
neural network for determining viewpoint coordinates on a
screen. Elektronika Ir Elektrotechnika 22 2 (2016) 86-91.
DOI: http://dx.doi.org/10.5755/j01.eie.22.2.7586
2. Lee WPO, Kaoli C, Huang JY. A smart TV system with
body-gesture control, tag-based rating and context-aware
recommendation. Knowledge-Based Systems 56 (2014)
167-178. https://doi.org/10.1016/j.knosys.2013.11.007
3. Bellmore C, Ptucha R, Savakis A. Interactıve dısplay using
depth and RGB sensors for face and gesture control. Western
New York Image Processing Workshop, 2011. https://doi.
org/10.1109/WNYIPW.2011.6122883.
4. Yavşan E, Uçar A. Gesture imitation and recognition using
Kinect sensor and extreme learning machines. Measurement
94 (2016) 852–861.
5. Rahman ASMM, Saboune J, Saddik AE. Motion-path
based in car gesture control of the multimedia devices.
DIVANet '11 Proceedings of the first ACM international
symposium on Design and analysis of intelligent vehicular
networks and applications 69-76, 2016. https://doi.
org/10.1145/2069000.2069013.
6. Bhuiyan M, Picking R. Gesture-controlled user interfaces,
what have we done and what’s next?. 2009. http://citeseerx.
ist.psu.edu/viewdoc/download?doi=10.1.1.562.6140&rep
=rep1&type=pdf. (accessed 13 11 2018).
7. Kela J, Korpipaa P, Mantyjarvi J, Kallio S, Savino G, Jozzo L,
Marca S. Accelerometer-based gesture control for a desing
environment. Personal and Ubiquitous Computing (2006)
285-299. https://doi.org/10.1007/s00779-005-0033-
8. Mantyjarvi J, Kela J, Korpipaa P, Kallio S. Enabling fast and
effortless customisation in accelerometer based gestureinteraction. MUM '04 Proceedings of the 3rd international
conference on Mobile and ubiquitous multimedia 25-31,
2004. https://doi.org/10.1145/1052380.1052385
9. Hackenberg G, McCall R, Broll W. Lightweight Palm and
Finger Tracking for Real-Time 3D Gesture Control. 2011
IEEE Virtual Reality Conference, 2011. https://doi.
org/10.1109/VR.2011.5759431.
10. Akyol S, Canzler U, Bengler K, Hahn W. Gesture control
for use in automobiles. IAPR Workshop on Machine Vision
Applications, Nov. 28-30, 2000. The University of Tokyo,
Japan. https://pdfs.semanticscholar.org/fb51/6222c7c
87f42872a28ff8fc74139447b1280.pdf. (accessed 13 11
2018).
11. Bizzotto N, Costanza A, Bizzotto L, Revis D, Sandri A,
Mangan B. Leap motion gesture control with osirix in the
operating room to control imaging. Surgical Innovation,
2014. https://doi.org/10.1177/1553350614528384.
12. Cohen CJ, Beach G, Foulk G. A Basic Hand Gesture Control
System for PC Applications. IEEE Xplore Digital Library,
2001. https://doi.org/10.1109/AIPR.2001.991206.
13. Gallo L, Placitelli AP, Ciampi M. Controller-free exploration
of medical image data: Experiencing the Kinect. 2011
24th International Symposium on Computer-Based
Medical Systems (CBMS), 2011. https://doi.org/10.1109/
CBMS.2011.5999138.
14. Doğan RÖ, Doğan H, Köse C. Virtual Mouse Control with Hand
Gesture Information Extraction and Tracking. 23nd Signal
Processing and Communications Applications Conference
(SIU) 2015. https://ieeexplore.ieee.org/stamp/stamp.
jsp?arnumber=7130228. (accessed 10 12 2019).
15. Kaura HK, Honrao V, Patil S, Shetty P. Gesture Controlled
Robot using Image Processing. (IJARAI) International
Journal of Advanced Research in Artificial Intelligence, Vol.
2, No. 5, 201, 2013 https://pdfs.semanticscholar.org/
ff0f/20e3dbbdf257ec3ca36be4ed251036b49e11.pdf.
(accessed 13 11 2018).
16. Chowdary PRV, Babu MN, Subbareddy TV, Reddy BM,
Elamaran V. Image Processing Algorithms for Gesture
Recognition using MATLAB. 2014 IEEE International
Conference on Advanced Communication Control and
Computing Technologies (ICACCCT), 2014. https://doi.
org/10.1109/ICACCCT.2014.7019356.
17. Kar S, Banerjee S, Jana A, Kundu D, Chatterjee D, Ghosh
S, Mitra D, Gupta SD. Image Processing Based Customized
Image Editor and Gesture Controlled Embedded Robot
Coupled with Voice Control Features. (IJACSA) International
Journal of Advanced Computer Science and Applications
6 (2015) 11. https://pdfs.semanticscholar.org/f000/
be3e91d69dc8ce1f87ae32ae7e5395b09b86.pdf.
(accessed 13 11 2018).
18. Osimani C, Piedra-Fernandez JA, Ojeda-Castelo JJ, Iribarne
L. Hand Posture Recognition with Standard Webcam for
Natural Interaction, WorldCIST 2017. Advances in Intelligent
Systems and Computing, vol 570 (2017) Springer.
19. Hsiang-Yueh L, Hao-Yuan K, Yu-Chun H. Real-time Hand
Gesture Recognition System and Application. Sensors and
Materials, Vol. 30, No. 4 (2018) 869–884.
20. Schacher JC. Gesture control is sounds in 3D space.
Proceedings of the 2007 Conference on New Interfaces for
Musical Expression (NIME07), New York, NY, USA, 2007.
http://www.nime.org/proceedings/2007/nime2007_358.
pdf. (accessed 13 11 2018).
21. Erden F. Hand gesture recognition using two differential
PIR sensors and a camera. 2014 22nd Signal Processing
and Communications Applications Conference (SIU), 2014.
https://doi.org/10.1109/SIU.2014.6830237.
22. Şahin A. Hacking the Gestures of Past for Future
Interactions. M.Sc. THESIS, 2013 http://muep.mau.se/
bitstream/handle/2043/15700/Atilim%20Sahin%20-%20
Hacking%20the%20Gestures%20of%20Past%20for%20
Future%20Interactions.pdf?sequence=2&isAllowed=y.
(accessed 13 11 2018).
23. Rautaray S, Agrawal A. Vision based hand gesture
recognition for human computer interaction: a survey. Artif.
Intell. Rev. 43 (2015) 1–54
24. Munir O, Ali AN, Javaan C. Hand Gesture Recognition Based
on Computer Vision: A Review of Techniques. Journal of
Imaging 6(8) 2020 73.
26. Optical flow-based gesture motion direction recognition
method, https://patents.google.com/patent/
CN104331151A/en
27. Pathak B, Jalal AS. Motion Direction Code—A Novel Feature
for Hand Gesture Recognition. Advances in Intelligent
Systems and Computing, vol 798 2019. Springer