Two-Stream Deep Learning Architecture-Based Human Action Recognition

作     者：Faheem Shehzad Muhammad Attique Khan Muhammad Asfand E.Yar Muhammad Sharif Majed Alhaisoni Usman Tariq Arnab Majumdar Orawit Thinnukool 

作者机构：Department of Computer ScienceCOMSATS University IslamabadWah CampusPakistan Department of Computer ScienceHITEC UniversityTaxilaPakistan Department of Computer ScienceBahria UniversityIslamabadPakistan Computer Sciences DepartmentCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and SciencePrince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Faculty of EngineeringImperial College LondonLondonSW72AZUK College of ArtsMediaand TechnologyChiang Mai UniversityChiang Mai50200Thailand 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第3期

页      面：5931-5949页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research work is supported in part by Chiang Mai University and HITEC University 

主　　题：Human action recognition deep learning transfer learning fusion of multiple features features optimization 

摘      要：Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer *** breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture *** HAR techniques are introduced in the ***,the challenge of redundant and irrelevant features reduces recognition *** also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among *** this work,a deep learning and improved whale optimization algorithm based framework is proposed for *** proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final *** pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition *** fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best *** best selected features are finally classified usingmachine learning(ML)*** publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%*** with state of the art techniques(SOTA),the proposed method showed the improved accuracy.