ItemConference Paper

Enhancing Video Surveillance for Crime Detection Using Anomaly Detection Techniques

Title
Enhancing Video Surveillance for Crime Detection Using Anomaly Detection Techniques
Creator
Chandu, Eerenti; Vinay Kumar, Kasthuri; Chandra Kumar, Kodavati; Snehith, Raja Venkat Sai; Prathap, Boppuru Rudra
Description
Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. The process of detecting similarities or data points that significantly depart from the norm or expected behavior of a given system is known as anomaly detection. Predictive maintenance, network intrusion detection, and fraud detection are just a few of the areas where anomaly detection is applied. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of experts to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. An anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. In this context, video surveillance refers to observing the scenes of improper human behaviors which are termed as real world anomalies. Depending on the availability of data sets, anomaly detection algorithms can be supervised, unsupervised, or semi- supervised. The quality of the data and the selection of the best algorithms determine how well anomaly detection techniques work. This paper proposes the use of anomaly detection techniques to enhance video surveillance systems for crime detection. By identifying unusual activities in surveillance footage, the system can alert authorities to potential criminal activity and improve overall security measures. The effectiveness of this approach is demonstrated through experiments and analysis of real-world surveillance data. 2025 Author(s).
Source
AIP Conference Proceedings;Volume;3175;Issue;1;Article No.;20070;
Date
01-01-2025
Publisher
American Institute of Physics
Coverage
Chandu E., Department of Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India; Vinay Kumar K., Department of Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India; Chandra Kumar K., Department of Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India; Snehith R.V.S., Department of Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India; Prathap B.R., Department of Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 0094243X;
Format
online
Language
English
Type
Conference paper

Citation

Chandu, Eerenti; Vinay Kumar, Kasthuri; Chandra Kumar, Kodavati; Snehith, Raja Venkat Sai; Prathap, Boppuru Rudra, “Enhancing Video Surveillance for Crime Detection Using Anomaly Detection Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25712.

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