NDLI: Video summarization guiding evaluative rectification for industrial activity recognition

Content Provider	IEEE Xplore Digital Library
Author	Voulodimos, A.S. Doulamis, A.D. Kosmopoulos, D.I. Varvarigou, T.A.
Copyright Year	2011
Description	Author affiliation: School of Electrical & Computer Engineering, National Technical University of Athens, Greece (Voulodimos, A.S.; Varvarigou, T.A.) \|\| Dept of Computer Science & Engineering, University of Texas at Arlington, USA (Kosmopoulos, D.I.) \|\| Dept of Production Engineering & Management, Technical University of Crete, Greece (Doulamis, A.D.)
Abstract	In this paper we present a video summarization method that extracts key-frames from industrial surveillance videos, thus dramatically reducing the number of frames without significant loss of semantic content. We propose to use the produced summaries as training set for neural network based Evaluative Rectification. Evaluative Rectification is a method that exploits an expert user's feedback regarding the correctness of an activity recognition framework on part of the data in order to enhance future classification results. The size of the training sample set usually depends on the topology of the network and on the complexity of the environment and activities observed. However, as is shown by the experiments conducted in a real-world industrial activity recognition dataset, using a much smaller but representative sample stemming from our summarization technique leads to significantly higher accuracy rates than those attained by a same size but randomly chosen set. To obtain comparable improvement in accuracy without the summarization technique, the experiments show that a far larger training sample set is needed, therefore requiring significantly increased human resources and computational cost.
Starting Page	950
Ending Page	957
File Size	7800464
Page Count	8
File Format	PDF
ISBN	9781467300629
e-ISBN	9781467300636
DOI	10.1109/ICCVW.2011.6130354
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Visualization Welding Hidden Markov models Vectors Complexity theory Biological neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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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