Improved Shark Smell Optimization Algorithm for Human Action Recognition  

作　　者：Inzamam Mashood Nasir Mudassar Raza Jamal Hussain Shah Muhammad Attique Khan Yun-Cheol Nam Yunyoung Nam 

机构地区：[1]Department of Computer Science,COMSATS University Islamabad,Wah Campus,Wah Cantt,47040,Pakistan [2]Department of Computer Science,HITEC University,Taxila,Pakistan [3]Department of Architecture,Joongbu University,Goyang,10279,South Korea [4]Department of ICT Convergence,Soonchunhyang University,Asan,31538,Korea

出　　处：《Computers, Materials & Continua》2023年第9期2667-2684,共18页计算机、材料和连续体（英文）

基　　金：supported by the Collabo R&D between Industry,Academy,and Research Institute(S3250534)funded by the Ministry of SMEs and Startups(MSS,Korea);the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00218176);the Soonchunhyang University Research Fund.

摘　　要：Human Action Recognition(HAR)in uncontrolled environments targets to recognition of different actions froma video.An effective HAR model can be employed for an application like human-computer interaction,health care,person tracking,and video surveillance.Machine Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature fusion.The accuracy and effectiveness of these models continue to be the biggest challenge in this field.In this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted features.This proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search space.The proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of FV.Once local optimal features are selected,a global search is conducted to further optimize these features.The proposed iSSO algorithm is employed on nine(9)selected CNN models.These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition.To evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected.

关 键 词：Action recognition improved shark smell optimization convolutional neural networks machine learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]