NDLI: Constructive, robust and adaptive OS-ELM in human action recognition

Content Provider	IEEE Xplore Digital Library
Author	Budiman, A. Fanany, M.I. Basaruddin, C.
Copyright Year	2014
Description	Author affiliation: Fac. of Comput. Sci., Univ. of Indonesia, Depok, Indonesia (Budiman, A.; Fanany, M.I.; Basaruddin, C.)
Abstract	We introduce a constructive, robust, and adaptive OS-ELM (Online Sequential Extreme Machine Learning) that combines learning strategies of Constructive Enhancement OSELM and Robust OS-ELM, and add adaptive capability to receive a new target class requirement during sequential learning and applied in human action recognition. The overall strategy is aimed to deal against parameters tuning and new requirements drift problem in the sequential learning process that commonly happened in human action recognition. Our proposed method has an automatic and systematic approach for determining input weight and bias value when the hidden nodes need to be increased to handle a larger training data size and to adjust the output weight when the new target class label is presented during sequential learning. We demonstrated the capability using 2013 Challenge on Multi modal Gesture Recognition open dataset (Chalearn 2013) with uni modal features (skeleton data) only. Our experiments using skeletons of upper body joints features with normalized euclidean distance and projection angle position coordinates to shoulder center and hip center, clustering analysis with k-means for pose based generation and Bag of Pose (BoP) for temporal sequential information. We developed the training sequence scenario to introduce the partial action classes in the initial training and the rest of training data with all classes in the next sequences to simulate the condition when the OS-ELM received new target class requirement drift. Our proposed method gives better accuracy plus adaptive capability compared with SUMO method which is the best uni modal method for chalearn data and still maintain a reasonably small computation time.
Starting Page	39
Ending Page	45
File Size	676038
Page Count	7
File Format	PDF
e-ISBN	9781479949090
DOI	10.1109/IAICT.2014.6922113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-28
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Learning systems Extreme learning machine (ELM) Adaptive learning Systematics Sequential Training data Robustness Feedforward neural networks Online Joints
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	Dr. B. Sutradhar bsutra@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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