| Autor: |
Algabri R; Research Institute of Engineering and Technology, Hanyang University, Ansan 15588, Korea.; School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea., Choi MT; School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Nov 02; Vol. 22 (21). Date of Electronic Publication: 2022 Nov 02. |
| DOI: |
10.3390/s22218422 |
| Abstrakt: |
It is challenging for a mobile robot to follow a specific target person in a dynamic environment, comprising people wearing similar-colored clothes and having the same or similar height. This study describes a novel framework for a person identification model that identifies a target person by merging multiple features into a single joint feature online. The proposed framework exploits the deep learning output to extract four features for tracking the target person without prior knowledge making it generalizable and more robust. A modified intersection over union between the current frame and the last frame is proposed as a feature to distinguish people, in addition to color, height, and location. To improve the performance of target identification in a dynamic environment, an online boosting method was adapted by continuously updating the features in every frame. Through extensive real-life experiments, the effectiveness of the proposed method was demonstrated by showing experimental results that it outperformed the previous methods. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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