Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection

Smriti Amatya; Ishika; M. V.Manoj Kumar; B. S. Prashanth; H. R. Sneha; Likewin Thomas; Vishnu Yarlagadda

doi:10.1007/978-981-16-1342-5_26

Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection

Smriti Amatya^*
, Ishika
, M. V.Manoj Kumar
, B. S. Prashanth
, H. R. Sneha
, Likewin Thomas
, Vishnu Yarlagadda

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Hand gesture is a way of communicating through sign languages and presently it has been a hot research topic in the field of Artificial intelligence and Data Science. Contemporary methods for detecting the hand gesture uses optical/mechanical sensors, actuator & accelerometer attached with the glove. This paper proposes a novel method for gesture identification for various hand gestures without using additional equipment (such as sensors or gloves). The proposed method captures the image of the hand gesture then segments it. The real-time hand gesture is tracked down by the convex hull. Convolutional Neural Network is used to detect the area of hand gestures in real-time. The results of the experimental evaluation of the proposed method gave 80% accuracy.

Original language	English
Title of host publication	Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020
Editors	N. R. Shetty, L. M. Patnaik, H. C. Nagaraj, Prasad N. Hamsavath, N. Nalini
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	325-335
Number of pages	11
ISBN (Print)	9789811613418
DOIs	https://doi.org/10.1007/978-981-16-1342-5_26
Publication status	Published - 2022
Event	6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020 - Bangalore, India Duration: 25-09-2020 → 26-09-2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	790
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020
Country/Territory	India
City	Bangalore
Period	25-09-20 → 26-09-20

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-16-1342-5_26

Cite this

Amatya, S., Ishika, Kumar, M. V. M., Prashanth, B. S., Sneha, H. R., Thomas, L., & Yarlagadda, V. (2022). Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection. In N. R. Shetty, L. M. Patnaik, H. C. Nagaraj, P. N. Hamsavath, & N. Nalini (Eds.), Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020 (pp. 325-335). (Lecture Notes in Electrical Engineering; Vol. 790). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-1342-5_26

Amatya, Smriti ; Ishika ; Kumar, M. V.Manoj et al. / Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection. Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. editor / N. R. Shetty ; L. M. Patnaik ; H. C. Nagaraj ; Prasad N. Hamsavath ; N. Nalini. Springer Science and Business Media Deutschland GmbH, 2022. pp. 325-335 (Lecture Notes in Electrical Engineering).

@inproceedings{ec50e9a1c5c5476986069c46e9104814,

title = "Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection",

abstract = "Hand gesture is a way of communicating through sign languages and presently it has been a hot research topic in the field of Artificial intelligence and Data Science. Contemporary methods for detecting the hand gesture uses optical/mechanical sensors, actuator \& accelerometer attached with the glove. This paper proposes a novel method for gesture identification for various hand gestures without using additional equipment (such as sensors or gloves). The proposed method captures the image of the hand gesture then segments it. The real-time hand gesture is tracked down by the convex hull. Convolutional Neural Network is used to detect the area of hand gestures in real-time. The results of the experimental evaluation of the proposed method gave 80\% accuracy.",

author = "Smriti Amatya and Ishika and Kumar, \{M. V.Manoj\} and Prashanth, \{B. S.\} and Sneha, \{H. R.\} and Likewin Thomas and Vishnu Yarlagadda",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020 ; Conference date: 25-09-2020 Through 26-09-2020",

year = "2022",

doi = "10.1007/978-981-16-1342-5\_26",

language = "English",

isbn = "9789811613418",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "325--335",

editor = "Shetty, \{N. R.\} and Patnaik, \{L. M.\} and Nagaraj, \{H. C.\} and Hamsavath, \{Prasad N.\} and N. Nalini",

booktitle = "Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020",

address = "Germany",

}

Amatya, S, Ishika, Kumar, MVM, Prashanth, BS, Sneha, HR, Thomas, L & Yarlagadda, V 2022, Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection. in NR Shetty, LM Patnaik, HC Nagaraj, PN Hamsavath & N Nalini (eds), Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. Lecture Notes in Electrical Engineering, vol. 790, Springer Science and Business Media Deutschland GmbH, pp. 325-335, 6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020, Bangalore, India, 25-09-20. https://doi.org/10.1007/978-981-16-1342-5_26

Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection. / Amatya, Smriti; Ishika; Kumar, M. V.Manoj et al.
Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. ed. / N. R. Shetty; L. M. Patnaik; H. C. Nagaraj; Prasad N. Hamsavath; N. Nalini. Springer Science and Business Media Deutschland GmbH, 2022. p. 325-335 (Lecture Notes in Electrical Engineering; Vol. 790).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection

AU - Amatya, Smriti

AU - Ishika,

AU - Kumar, M. V.Manoj

AU - Prashanth, B. S.

AU - Sneha, H. R.

AU - Thomas, Likewin

AU - Yarlagadda, Vishnu

PY - 2022

Y1 - 2022

N2 - Hand gesture is a way of communicating through sign languages and presently it has been a hot research topic in the field of Artificial intelligence and Data Science. Contemporary methods for detecting the hand gesture uses optical/mechanical sensors, actuator & accelerometer attached with the glove. This paper proposes a novel method for gesture identification for various hand gestures without using additional equipment (such as sensors or gloves). The proposed method captures the image of the hand gesture then segments it. The real-time hand gesture is tracked down by the convex hull. Convolutional Neural Network is used to detect the area of hand gestures in real-time. The results of the experimental evaluation of the proposed method gave 80% accuracy.

AB - Hand gesture is a way of communicating through sign languages and presently it has been a hot research topic in the field of Artificial intelligence and Data Science. Contemporary methods for detecting the hand gesture uses optical/mechanical sensors, actuator & accelerometer attached with the glove. This paper proposes a novel method for gesture identification for various hand gestures without using additional equipment (such as sensors or gloves). The proposed method captures the image of the hand gesture then segments it. The real-time hand gesture is tracked down by the convex hull. Convolutional Neural Network is used to detect the area of hand gestures in real-time. The results of the experimental evaluation of the proposed method gave 80% accuracy.

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Amatya S, Ishika, Kumar MVM, Prashanth BS, Sneha HR, Thomas L et al. Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection. In Shetty NR, Patnaik LM, Nagaraj HC, Hamsavath PN, Nalini N, editors, Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 325-335. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-1342-5_26

Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection

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