TY - GEN

T1 - Analyzing sedentary behavior in life-logging images

AU - Moghimi, Mohammad

AU - Wu, Wanmin

AU - Chen, Jacqueline

AU - Godbole, Suneeta

AU - Marshall, Simon

AU - Kerr, Jacqueline

AU - Belongie, Serge

N1 - Publisher Copyright: © 2014 IEEE.

PY - 2014/1/28

Y1 - 2014/1/28

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Large Scale Image Analysis

KW - Visual Classification

KW - Wearable camera

UR - http://www.scopus.com/inward/record.url?scp=84949929440&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2014.7025202

DO - 10.1109/ICIP.2014.7025202

M3 - Conference article

AN - SCOPUS:84949929440

SP - 1011

EP - 1015

JO - 2014 IEEE International Conference on Image Processing, ICIP 2014

JF - 2014 IEEE International Conference on Image Processing, ICIP 2014

ER -