کنفرانس مهندسی برق ایران

صفحه اصلی / بیست و نهمین کنفرانس مهندسی برق ایران

Transfer Learning Based Method for Human Activity Recognition

نویسندگان :

Saeedeh Zebhi¹ Smt Almodarresi² Vahid Abootalebi³

1- Yazd university 2- Yazd university 3- Yazd university

کلمات کلیدی :

gait history image, gradient, human activity recognition, transfer learning

چکیده :

A gait history image (GHI) is a spatial template that accumulates regions of motion into a single image in which moving pixels are brighter than others. A new descriptor named Time-sliced averaged gradient boundary magnitude (TAGBM) is also designed to show the time variations of motion. In the proposed method, each video is split into N and M groups of consecutive frames, and the GHI and TAGBM are computed for each group, resulting spatial and temporal templates. Transfer learning with the fine-tuning technique has been used for classifying these templates. This proposed method achieves the recognition accuracies of 96.5% and 92.7% for KTH and UCF Sport action datasets, respectively. Also it is compared with state-of-the-art approaches and the results demonstrate that the proposed method has the best efficiency.