Open Access

ARTICLE

Anomalous Situations Recognition in Surveillance Images Using Deep Learning

by Qurat-ul-Ain Arshad¹, Mudassar Raza¹, Wazir Zada Khan², Ayesha Siddiqa², Abdul Muiz², Muhammad Attique Khan^3,*, Usman Tariq⁴, Taerang Kim⁵, Jae-Hyuk Cha^5,*

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

* Corresponding Authors: Muhammad Attique Khan. Email: ; Jae-Hyuk Cha. Email:

(This article belongs to the Special Issue: Multimedia Encryption and Information Security)

Computers, Materials & Continua 2023, 76(1), 1103-1125. https://doi.org/10.32604/cmc.2023.039752

Received 14 February 2023; Accepted 20 April 2023; Issue published 08 June 2023

Abstract

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, differentiating 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.

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Keywords

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