Open Access

ARTICLE

Abnormality Identification in Video Surveillance System using DCT

A. Balasundaram^1,*, Golda Dilip², M. Manickam³, Arun Kumar Sivaraman⁴, K. Gurunathan⁵, R. Dhanalakshmi⁶, S. Ashokkumar⁷

1 Centre for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, 600127, India
2 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, 600026, India
3 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India
4 School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, 600127, India
5 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, 639113, India
6 Department of Computer Science and Engineering, KCG College of Technology, Chennai, 600097, India
7 Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105, India

* Corresponding Author: A. Balasundaram. Email:

(This article belongs to this Special Issue: Deep Neural Network for Intelligent Systems)

Intelligent Automation & Soft Computing 2022, 32(2), 693-704. https://doi.org/10.32604/iasc.2022.022241

Received 01 August 2021; Accepted 02 September 2021; Issue published 17 November 2021

Abstract

In the present world, video surveillance methods play a vital role in observing the activities that take place across secured and unsecured environment. The main aim with which a surveillance system is deployed is to spot abnormalities in specific areas like airport, military, forests and other remote areas, etc. A new block-based strategy is represented in this paper. This strategy is used to identify unusual circumstances by examining the pixel-wise frame movement instead of the standard object-based approaches. The density and also the speed of the movement is extorted by utilizing optical flow. The proposed strategy recognizes the unusual movement and differences by using discrete cosine transform coefficient. Our goal is to attain a trouble-free block-based Discrete Cosine Transform (DCT) strategy that promotes real-time abnormality detection. The proposed approach has been evaluated against an airport dataset and the outcome of unusual happenings occurred in is evaluated and reported.

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Citations