A Gait Recognition Method for Human Following in Service Robots
提出一种步态识别方法，通过分割行走序列并提取混合步态特征，帮助服务机器人识别并跟随特定人员，在25人数据集上验证了有效性。
In this paper, we propose a gait recognition method for service robots to conduct human following tasks. A walking sequence segmentation method is designed to extract the consecutive gait cycles from an arbitrary walking sequence. Based on the segmentation results, a novel hybrid gait feature is proposed to capture the static, dynamic, and trajectory features for each segmented key and supplementary gait cycles. A dataset of 25 human subjects is collected to evaluate the proposed method in three different walking paths with various walking directions. Experimental results show that the proposed method achieves satisfactory performance in terms of identification accuracy and Fcomb indexes on our dataset. Compared with five state-of-the-art gait recognition methods, the proposed method achieves the best performance on human gait recognition based on the walking sequences defined in our proposed dataset.