Real-time Joint Based Human Activity Recognition using RGB-Depth Camera
| Main Authors: | Omer Faruk Ince, Ibrahim Furkan Ince, Jang Sik Park, Jong-Kwan Song |
|---|---|
| Format: | Proceeding |
| Terbitan: |
, 2017
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1068141 |
Daftar Isi:
- Human activity recognition (HAR) has gained an effective role for computer vision in the problem of video surveillance systems and medical purposes. This study represents an approach for a biometric system that can recognize human activities in 3D space. Related approaches have shown that monitoring of various physiological signals can provide distinctive information to recognize human activities. The proposed method conducts a machine learning to determine a pattern on numerous activities using angles between joints in 3D space. Basically, activity related joint angles are obtained using Microsoft Kinect v2 sensor. Since HAR is operated in a time domain, joints’ angle information is stored in every 20 frames. Stored information consist the feature set for human activity recognition. Support vector machine (SVM) is used for both train and test. Among six different human activities, obtained average accuracy is 99,999482 %.