NDLI: Implementation and comparison of three architectures for gesture recognition

Content Provider	IEEE Xplore Digital Library
Author	Corradini, A. Gross, H.-M.
Copyright Year	2000
Description	Author affiliation: Dept. of Neuroinf., Tech. Hochschule Ilmenau, Germany (Corradini, A.)
Abstract	Several systems for automatic gesture recognition have been developed using different strategies and approaches. In these systems the recognition engine is mainly based on three algorithms: dynamic pattern matching, statistical classification, and neural networks (NN). In this paper three architectures for the recognition of dynamic gestures using the above mentioned techniques or a hybrid combination of them are presented and compared. For all architectures a common preprocessor receives as input a sequence of color images, and produces as output a sequence of feature vectors of continuous parameters. The first two systems are hybrid architectures consisting of a combination of neural networks and hidden Markov models (HMM). NNs are used for the classification of single feature vectors while HMMs for the modeling of sequences of them with the aim to exploit the properties of both these tools. More precisely, in the first system a Kohonen feature map (SOM) clusters the input space. Further, each code-book is transformed into a symbol from a discrete alphabet and fed into a discrete HMM for classification. In the second approach a radial basis function (RBF) network is directly used to compute the HMM state observation probabilities. In the last system only dynamic programming techniques are employed. An input sequence of feature vectors is matched by some predefined templates by using the dynamic time warping (DTW) algorithm. Preliminary experiments with our baseline systems achieved a recognition accuracy up to 92%. All systems use input from a monocular color video camera, are user-independent but so far, they are not yet real-time.
Starting Page	2361
Ending Page	2364
File Size	482070
Page Count	4
File Format	PDF
ISBN	0780362934
ISSN	15206149
DOI	10.1109/ICASSP.2000.859315
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-06-05
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Neural networks Pattern recognition Engines Heuristic algorithms Pattern matching Data preprocessing Color Computer networks Dynamic programming
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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