Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences
作者机构：University of WahWah Cantt47040Pakistan National University of Technology(NUTECH)Islamabad44000Pakistan COMSATS University IslamabadWah CampusWah CanttPakistan Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway Department of Computer Science and EngineeringSoonchunhyang UniversityAsan31538Korea Department of MathematicsUniversity of LeicesterLeicesterUK
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2021年第68卷第8期
页 面：2693-2709页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea Government(MOTIE)(P0012724,The Competency,Development Program for Industry Specialist) the Soonchunhyang University Research Fund
主 题：Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet
摘 要：Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.
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