An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7386
• Main Authors: Lu, Ying, Yu, Yifan, Lv, Xifan, Tao, Tingting
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 28.09.2022; MDPI
• Subjects: Accident prevention; Accuracy; Algorithms; Analysis; Behavior; Comparative analysis; dynamic time warping (DTW); eigenvectors; Emergency communications systems; Feature recognition; Identification; joint angles; Joints (anatomy); Methods; metro passenger; Passengers; Pelvis; Similarity; unsafe behavior recognition; China
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22197386

In order to solve the problem of the low action recognition accuracy of passengers’ unsafe behaviors caused by redundant joints, this study proposes an efficient recognition method based on a Kinect sensor. The method uses the pelvis as the starting point of the vector and high-frequency bone joints as the end point to construct the recognition feature vector. The joint angle difference between actions is obtained by using the cosine law, and the initial test result is converted into action similarity combined with the DTW similarity algorithm. Considering the combination of 3 angle features and 4 joint feature selection methods, 12 combined recognition models are formed. A comparative experiment was carried out to identify five types of unsafe behaviors of metro passengers—punch, one-armed call for help, arms for help, trip forward and trip backwards. The results show that the overall selection of joints has a poor similarity effect and cannot achieve the purpose of recognition. The overall recognition model effect of the local “pelvis divergence method” is higher than that of the local “adjacent joint method”, and the local “pelvis divergence method” has the highest recognition result of the maximum angle difference model, and the recognition results of the five unsafe behaviors are 86.9%, 89.2%, 85.5%, 86.7%, and 88.3%, respectively, and the recognition accuracy of this method is 95.7%, indicating the feasibility of the model. The recognition results are more concentrated and more stable, which significantly improves the recognition rate of metro passengers’ unsafe behavior.