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Entropy 2019, 21(4), 329; https://doi.org/10.3390/e21040329

Detecting Toe-Off Events Utilizing a Vision-Based Method

1
School of Forensic Science, People’s Public Security University of China, Beijing 100000, China
2
School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100000, China
3
School of Information Engineering and Network Security, People’s Public Security University of China, Beijing 100000, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210000, China
5
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210000, China
*
Authors to whom correspondence should be addressed.
Received: 15 February 2019 / Revised: 21 March 2019 / Accepted: 24 March 2019 / Published: 27 March 2019
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
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Abstract

Detecting 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
Keywords: toe-off detection; gait event; silhouettes difference; convolutional neural network toe-off detection; gait event; silhouettes difference; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tang, Y.; Li, Z.; Tian, H.; Ding, J.; Lin, B. Detecting Toe-Off Events Utilizing a Vision-Based Method. Entropy 2019, 21, 329.

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