Performance analysis of human gesture recognition techniques

Mansi Goyal; Bhavya Shahi; K. V. Prema; N. V.S.Subba Reddy

doi:10.1109/RTEICT.2017.8256568

Performance analysis of human gesture recognition techniques

Mansi Goyal
, Bhavya Shahi
, K. V. Prema
, N. V.S.Subba Reddy

School of Computer Engineering

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

4 Citations (Scopus)

Abstract

Today, a lot of research is being done in the area of human gesture recognition due to its various uses such as traffic management, surveillance, healthcare management etc. In this paper the performance of different filters, namely Gabor and Canny for edge detection during preprocessing of image sequences has been studied. Subsequently, the preprocessed images are used for human gesture recognition. The focus is mainly on two gestures-walk and bend. The different classifiers that are used are KNN (K-Nearest Neighbour), NN (Nearest Neighbour) and SVM (Support Vector Machine). The results are then compared for different training dataset sizes for each model. It is found that in general, the Gabor filter gave better results than the Canny edge detection method.

Original language	English
Title of host publication	RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	111-115
Number of pages	5
Volume	2018-January
ISBN (Electronic)	9781509037049
DOIs	https://doi.org/10.1109/RTEICT.2017.8256568
Publication status	Published - 12-01-2017
Event	2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017 - Bangalore, India Duration: 19-05-2017 → 20-05-2017

Conference

Conference	2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017
Country/Territory	India
City	Bangalore
Period	19-05-17 → 20-05-17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Media Technology
Control and Optimization
Instrumentation
Transportation
Communication

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10.1109/RTEICT.2017.8256568

Cite this

Goyal, M., Shahi, B., Prema, K. V., & Reddy, N. V. S. S. (2017). Performance analysis of human gesture recognition techniques. In RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings (Vol. 2018-January, pp. 111-115). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTEICT.2017.8256568

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title = "Performance analysis of human gesture recognition techniques",

abstract = "Today, a lot of research is being done in the area of human gesture recognition due to its various uses such as traffic management, surveillance, healthcare management etc. In this paper the performance of different filters, namely Gabor and Canny for edge detection during preprocessing of image sequences has been studied. Subsequently, the preprocessed images are used for human gesture recognition. The focus is mainly on two gestures-walk and bend. The different classifiers that are used are KNN (K-Nearest Neighbour), NN (Nearest Neighbour) and SVM (Support Vector Machine). The results are then compared for different training dataset sizes for each model. It is found that in general, the Gabor filter gave better results than the Canny edge detection method.",

author = "Mansi Goyal and Bhavya Shahi and Prema, \{K. V.\} and Reddy, \{N. V.S.Subba\}",

year = "2017",

month = jan,

day = "12",

doi = "10.1109/RTEICT.2017.8256568",

language = "English",

volume = "2018-January",

pages = "111--115",

booktitle = "RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017 ; Conference date: 19-05-2017 Through 20-05-2017",

}

Goyal, M, Shahi, B, Prema, KV & Reddy, NVSS 2017, Performance analysis of human gesture recognition techniques. in RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 111-115, 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017, Bangalore, India, 19-05-17. https://doi.org/10.1109/RTEICT.2017.8256568

Performance analysis of human gesture recognition techniques. / Goyal, Mansi; Shahi, Bhavya; Prema, K. V. et al.
RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 111-115.

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

TY - GEN

T1 - Performance analysis of human gesture recognition techniques

AU - Goyal, Mansi

AU - Shahi, Bhavya

AU - Prema, K. V.

AU - Reddy, N. V.S.Subba

PY - 2017/1/12

Y1 - 2017/1/12

N2 - Today, a lot of research is being done in the area of human gesture recognition due to its various uses such as traffic management, surveillance, healthcare management etc. In this paper the performance of different filters, namely Gabor and Canny for edge detection during preprocessing of image sequences has been studied. Subsequently, the preprocessed images are used for human gesture recognition. The focus is mainly on two gestures-walk and bend. The different classifiers that are used are KNN (K-Nearest Neighbour), NN (Nearest Neighbour) and SVM (Support Vector Machine). The results are then compared for different training dataset sizes for each model. It is found that in general, the Gabor filter gave better results than the Canny edge detection method.

AB - Today, a lot of research is being done in the area of human gesture recognition due to its various uses such as traffic management, surveillance, healthcare management etc. In this paper the performance of different filters, namely Gabor and Canny for edge detection during preprocessing of image sequences has been studied. Subsequently, the preprocessed images are used for human gesture recognition. The focus is mainly on two gestures-walk and bend. The different classifiers that are used are KNN (K-Nearest Neighbour), NN (Nearest Neighbour) and SVM (Support Vector Machine). The results are then compared for different training dataset sizes for each model. It is found that in general, the Gabor filter gave better results than the Canny edge detection method.

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U2 - 10.1109/RTEICT.2017.8256568

DO - 10.1109/RTEICT.2017.8256568

M3 - Conference contribution

VL - 2018-January

SP - 111

EP - 115

BT - RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017

Y2 - 19 May 2017 through 20 May 2017

ER -

Performance analysis of human gesture recognition techniques

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UN SDGs

All Science Journal Classification (ASJC) codes

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