Name
Abstract | ||
|---|---|---|
The unique, bi-pedal motion of humans has been shown to generate a characteristic micro-Doppler signature in the time-frequency domain that can be used to discriminate humans from not just other targets, but also between different activities, such as walking and running. In the literature, many different features have been proposed for classification applications. However, it is not known which features have a greater impact on classification performance, or indeed how many features should be used to achieve good classification. In this work, the mutual information of features extracted from human micro-Doppler signatures is computed. Taking the problem of classifying human arm-swing as an example, the features extracted are ordered in terms of importance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SIU.2013.6531440 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
Doppler effect,feature extraction,image classification,time-frequency analysis,bipedal motion,classification performance,feature extraction,human arm-swing classification,human classification,human microDoppler signature,mutual information,running,time-frequency domain,walking,feature extraction,human classification,information theory,micro-Doppler | Computer vision,Pattern recognition,Computer science,Feature (computer vision),Speech recognition,Feature extraction,Mutual information,Artificial intelligence,Time–frequency analysis,Doppler effect,Contextual image classification | Conference |
ISSN | ISBN | Citations |
2165-0608 | 978-1-4673-5561-2 | 1 |
PageRank | References | Authors |
0.43 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Burkan Tekeli | 1 | 7 | 2.33 |
| Sevgi Zubeyde Gurbuz | 2 | 18 | 3.86 |
| M. Yuksel | 3 | 261 | 17.19 |