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صفحه اصلی
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بیست و نهمین کنفرانس مهندسی برق ایران
Transfer Learning Based Method for Human Activity Recognition
نویسندگان :
Saeedeh Zebhi
1
Smt Almodarresi
2
Vahid Abootalebi
3
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.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.6.0
