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Proceedings Paper

Webcam classification using simple features
Author(s): Thitiporn Pramoun; Jeehyun Choe; He Li; Qingshuang Chen; Thumrongrat Amornraksa; Yung-Hsiang Lu; Edward J. Delp III
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Paper Abstract

Thousands of sensors are connected to the Internet and many of these sensors are cameras. The “Internet of Things” will contain many “things” that are image sensors. This vast network of distributed cameras (i.e. web cams) will continue to exponentially grow. In this paper we examine simple methods to classify an image from a web cam as “indoor/outdoor” and having “people/no people” based on simple features. We use four types of image features to classify an image as indoor/outdoor: color, edge, line, and text. To classify an image as having people/no people we use HOG and texture features. The features are weighted based on their significance and combined. A support vector machine is used for classification. Our system with feature weighting and feature combination yields 95.5% accuracy.

Paper Details

Date Published: 12 March 2015
PDF: 12 pages
Proc. SPIE 9401, Computational Imaging XIII, 94010G (12 March 2015); doi: 10.1117/12.2083417
Show Author Affiliations
Thitiporn Pramoun, King Mongkut’s Univ. of Technology Thonburi (Thailand)
Jeehyun Choe, Purdue Univ. (United States)
He Li, Purdue Univ. (United States)
Qingshuang Chen, Purdue Univ. (United States)
Thumrongrat Amornraksa, King Mongkut’s Univ. of Technology Thonburi (Thailand)
Yung-Hsiang Lu, Purdue Univ. (United States)
Edward J. Delp III, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 9401:
Computational Imaging XIII
Charles A. Bouman; Ken D. Sauer, Editor(s)

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