Research Institute of Engineering and Technology, Hanyang University, Ansan 15588, Korea.
School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea.
Sensors (Basel). 2022 Nov 2;22(21):8422. doi: 10.3390/s22218422.
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.
在动态环境中，对于移动机器人来说，跟踪特定的目标人物是一项具有挑战性的任务，因为在这种环境中，人们穿着相似颜色的衣服，身高相同或相似。本研究描述了一种新的人员识别模型框架，该框架通过将多个特征合并为单个联合特征在线识别目标人员。所提出的框架利用深度学习输出提取四个特征来跟踪目标人员，而无需事先了解，从而使其具有通用性和更强的鲁棒性。除了颜色、高度和位置之外，还提出了当前帧和上一帧之间的改进的交并比作为区分人员的特征。为了提高动态环境中目标识别的性能，通过在每帧中不断更新特征，采用了在线提升方法。通过广泛的实际实验，通过展示实验结果证明了所提出方法的有效性，表明它优于以前的方法。
