An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities  

作　　者：Muazzam MAQSOOD Sadaf YASMIN Saira GILLANI Maryam BUKHARI Seungmin RHO Sang-Soo YEO 

机构地区：[1]Department of Computer Science,COMSATS University Islamabad,Attock Campus,Attock 43600,Pakistan [2]Department of Computer Science,Bahria University,Lahore 54600,Pakistan [3]Department of Industrial Security,Chung-Ang University,Seoul 06974,Republic of Korea [4]Department of Computer Engineering,Mokwon University,Daejeon 35349,Republic of Korea

出　　处：《Frontiers of Computer Science》2023年第4期83-96,共14页中国计算机科学前沿（英文版）

基　　金：supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0008703,The Competency Development Program for Industry Specialist);the MSIT(Ministry of Science and ICT),Republic of Korea,under the ITRC(Information Technology Research Center)support program(IITP-2022-2018-0-01799)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).

摘　　要：Innovations on the Internet of Everything(IoE)enabled systems are driving a change in the settings where we interact in smart units,recognized globally as smart city environments.However,intelligent video-surveillance systems are critical to increasing the security of these smart cities.More precisely,in today’s world of smart video surveillance,person re-identification(Re-ID)has gained increased consideration by researchers.Various researchers have designed deep learningbased algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems.In this line of research,we designed an adaptive feature refinementbased deep learning architecture to conduct person Re-ID.In the proposed architecture,the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention.In addition,the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps.Furthermore,the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets.When compared with existing approaches,the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%,respectively.

关 键 词：Internet of Everything(IoE) visual surveillance systems big data security systems person re-identification(Re-ID) deep learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]