Webcam classification using simple features
Conference proceedings article
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Publication Details
Author list: Pramoun T., Choe J., Li H., Chen Q., Amornraksa T., Lu Y.-H., Delp E.J.
Publisher: Society of Photo-optical Instrumentation Engineers
Publication year: 2015
Volume number: 9401
ISBN: 9781628414912
ISSN: 0277-786X
eISSN: 1996-756X
Languages: English-Great Britain (EN-GB)
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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. ฉ 2015 SPIE-IS & T.
Keywords
dominant color descriptor, gradient magnitude, nearest neighbor, number of edge
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