Webcam classification using simple features

Thitiporn Pramoun; Jeehyun Choe; He Li; Qingshuang Chen; Thumrongrat Amornraksa; Yung-Hsiang Lu; Edward J. Delp III

doi:10.1117/12.2083417

12 March 2015 Webcam classification using simple features

Thitiporn Pramoun, Jeehyun Choe, He Li, Qingshuang Chen, Thumrongrat Amornraksa, Yung-Hsiang Lu, Edward J. Delp III

Proceedings Volume 9401, Computational Imaging XIII; 94010G (2015) https://doi.org/10.1117/12.2083417
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Thitiporn Pramoun, Jeehyun Choe, He Li, Qingshuang Chen, Thumrongrat Amornraksa, Yung-Hsiang Lu, and Edward J. Delp III "Webcam classification using simple features", Proc. SPIE 9401, Computational Imaging XIII, 94010G (12 March 2015); https://doi.org/10.1117/12.2083417

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