Anomalous Situations Recognition in Surveillance Images Using Deep Learning  

作　　者：Qurat-ul-Ain Arshad Mudassar Raza Wazir Zada Khan Ayesha Siddiqa Abdul Muiz Muhammad Attique Khan Usman Tariq Taerang Kim Jae-Hyuk Cha 

机构地区：[1]Department of Computer Science,COMSATS University Islamabad,Wah Campus,47040,Pakistan [2]Deptartment of Computer Science,University of Wah,Wah Cantt,47040,Pakistan [3]Deptartment of Computer Science,HITEC University,Taxila,47080,Pakistan [4]Management Information System Department,College of Business Administration,Prince Sattam Bin Abdulaziz University,Al-Kharj 16278,Saudi Arabia [5]Department of Computer Science,Hanyang University,Seoul,04763,Korea

出　　处：《Computers, Materials & Continua》2023年第7期1103-1125,共23页计算机、材料和连续体（英文）

基　　金：supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP);granted financial resources from the Ministry of Trade,Industry Energy,Republic ofKorea.(No.20204010600090).

摘　　要：Anomalous situations in surveillance videos or images that may result in security issues,such as disasters,accidents,crime,violence,or terrorism,can be identified through video anomaly detection.However,differentiat-ing anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations,busy sporting fields,airports,shopping areas,military bases,care centers,etc.Deep learning models’learning capability is leveraged to identify abnormal situations with improved accuracy.This work proposes a deep learning architecture called Anomalous Situation Recognition Network(ASRNet)for deep feature extraction to improve the detection accuracy of various anomalous image situations.The proposed framework has five steps.In the first step,pretraining of the proposed architecture is performed on the CIFAR-100 dataset.In the second step,the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset.In the third step,serial feature fusion is performed,and then the Dragonfly algorithm is utilized for feature optimization in the fourth step.Finally,using optimized features,various Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based classification models are utilized to detect anomalous situations.The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000.The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24%using cubic SVM.

关 键 词：Anomaly detection anomalous events anomalous behavior anomalous objects violence detection deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]