Detecting Toe-Off Events Utilizing a Vision-Based Method
AbstractDetecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy. View Full-Text
Share & Cite This Article
Tang, Y.; Li, Z.; Tian, H.; Ding, J.; Lin, B. Detecting Toe-Off Events Utilizing a Vision-Based Method. Entropy 2019, 21, 329.
Tang Y, Li Z, Tian H, Ding J, Lin B. Detecting Toe-Off Events Utilizing a Vision-Based Method. Entropy. 2019; 21(4):329.Chicago/Turabian Style
Tang, Yunqi; Li, Zhuorong; Tian, Huawei; Ding, Jianwei; Lin, Bingxian. 2019. "Detecting Toe-Off Events Utilizing a Vision-Based Method." Entropy 21, no. 4: 329.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.