Media Control Using Hand Gestures
DOI:
https://doi.org/10.54060/JIEEE/002.01.005Keywords:
Artificial Intelligence, Deep Learning, Computer Vision, OpenCV, PythonAbstract
The corporate world today basically relies on presentations of ideas and statistics. In the board room, the presenters are highly conscious of depicting confidence in their presen-tation. This would entail accessibility and mobility to the presenter or media viewer. As the extent of Artificial Intelligence is increasing in all directions, I am utilizing its extreme capabilities to create a software that would help in accessibility and save time and money. This paper describes a software written in Python 3.8 and makes use of Python libraries like OpenCV and PyAutoGUI to receive input from the computer’s Webcam and recognize gestures to control the PowerPoint Presentation and Portable Document For-mat (PDF) files or the Media Control. The user interface is built with the Python library PyQt5. This paper aims are to help people control their Presentations and Portable Document Format (PDF) files, and many other media through their hand gestures, without using a mouse or any other pointing device. The software would not require any other external hardware; hence it would not burn a hole in people’s pockets.
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M. Aamir, M. Irfan, T. Ali. et al., “An adoptive threshold-based multi-level deep convolutional neural network for glaucoma eye disease detection and classification,” Diagnostics (Basel), vol. 10, no. 8, Aug 2020.
N. N. Shende and S. P. Syed Ibrahim, “Layout detection using computer vision,” Int. j. comput. complex. intell. algorithms, vol. 1, no. 2, pp. 165-177, Nov 2019.
J. Bertling, “Exercising the ecological imagination: Representing the future of place,” Art educ., vol. 66, no. 1, pp. 33–39, 2013.
T. Kawasaki, “Theory of chromatography of macromolecules with rigid structures on hydroxyapatite columns. II. Dynamic part,” Biopolymers, vol. 9, no. 3, pp. 291–306, March 1970.
N. M. Jones, G. S. Paschos, B. Shrader, et al., “An overlay architecture for throughput optimal multipath routing,” in Proceedings of the 15th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc ’14, pp. 73-82, Aug 2014.
A. Anderson, “A framework for NPD management: doing the right things, doing them right, and measuring the results,” Trends Food Sci. Technol., vol. 19, no. 11, pp. 553–561, Nov 2008.
S. McInnis and A. Agrawal, “Web-based visualization and navigation of the content of SNOMED CT,” in International Conference on Advances in Computing and Communication Engineering (ICACCE), June 2018.
M. N. Lambani and Z. Nengome, “Group work impact on academic communication: Female English student teachers’ views,” Int. J. Educ. Sci., vol.18, no.1–3, pp.101–109, Sep 2017.
C. P. Papageorgiou, M. Oren, and T. Poggio, “A general framework for object detection,” in Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp. 555-562, Aug 2002.
G. Xie and W. Lu, “Image edge detection based on opencv,” Int. j. electron. electr. eng., vol. 1, no. 2, pp. 104–106, June 2013.
M. Rupali and P. Amit, “A review paper on general concepts of ‘artificial intelligence and machine learning,’” Int. adv. res. j. sci. eng. technol., vol. 4, no. 4, pp. 79–82, Jan 2017.
V. Wiley and T. Lucas, “Computer vision and image processing: A paper review,” Int. J. Artif. Intell. Res., vol. 2, no. 1, pp. 29-36, 2018.
H. Adusumalli, D. Kalyani, R. K. Sri, et al., “Face mask detection using OpenCV,” in Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 1304-1309, Feb 2021.
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