Analyzing sedentary behavior in life-logging images
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
We describe a study that aims to understand physical activity and sedentary behavior in free-living settings. We employed a wearable camera to record 3 to 5 days of imaging data with 40 participants, resulting in over 360,000 images. These images were then fully annotated by experienced staff with a rigorous coding protocol. We designed a deep learning based classifier in which we adapted a model that was originally trained for ImageNet [1]. We then added a spatio-temporal pyramid to our deep learning based classifier. Our results show our proposed method performs better than the state-of-the-art visual classification methods on our dataset. For most of the labels our system achieves more than 90% average accuracy across different individuals for frequent labels and more than 80% average accuracy for rare labels.
Originalsprog | Engelsk |
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Tidsskrift | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
Sider (fra-til) | 1011-1015 |
Antal sider | 5 |
DOI | |
Status | Udgivet - 28 jan. 2014 |
Eksternt udgivet | Ja |
Bibliografisk note
Publisher Copyright:
© 2014 IEEE.
ID: 302043908