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Unveiling Rare Patterns: Anomaly Detection in CCTV Footage for Safeguarding Home Premises Cover

Unveiling Rare Patterns: Anomaly Detection in CCTV Footage for Safeguarding Home Premises

Journal of Information Assurance and Security
Volume 19 (2024): Issue 1 (August 2024)
By: Mintu Movi and  Abdul Jabbar P  
Open Access
|Aug 2024

Figures & Tables

Figure 1.

SVM on Dataset 1
SVM on Dataset 1

Figure 2.

SVM on Dataset 2
SVM on Dataset 2

Figure 3.

SVM on Dataset 3
SVM on Dataset 3

Figure 4.

Isolation Forest on Dataset 1
Isolation Forest on Dataset 1

Figure 5.

Isolation Forest on Dataset 2
Isolation Forest on Dataset 2

Figure 6.

Isolation Forest on Dataset 3
Isolation Forest on Dataset 3

Figure 7.

Autoencoder-based Analysis on Dataset 1
Autoencoder-based Analysis on Dataset 1

Figure 8.

Autoencoder-based Analysis on Dataset 2
Autoencoder-based Analysis on Dataset 2

Figure 9.

Autoencoder-based Analysis on Dataset 3
Autoencoder-based Analysis on Dataset 3

Figure 10.

Local Outlier Factor on Dataset 1
Local Outlier Factor on Dataset 1

Figure 11.

Local Outlier Factor on Dataset 2
Local Outlier Factor on Dataset 2

Figure 12.

Local Outlier Factor on Dataset 3
Local Outlier Factor on Dataset 3

Figure 13.

Cluster-based Analysis on Dataset 1
Cluster-based Analysis on Dataset 1

Figure 14.

Cluster-based Analysis on Dataset 2
Cluster-based Analysis on Dataset 2

Figure 15.

Cluster-based Analysis on Dataset 3
Cluster-based Analysis on Dataset 3

Figure 16.

Agglomerative Clustering on Dataset 1.
Agglomerative Clustering on Dataset 1.

Figure 17.

Agglomerative Clustering on Dataset 2.
Agglomerative Clustering on Dataset 2.

Figure 18.

Agglomerative Clustering on Dataset 3.
Agglomerative Clustering on Dataset 3.

Figure 19.

Divisive Clustering on Dataset 1.
Divisive Clustering on Dataset 1.

Figure 20.

Divisive Clustering on Dataset 2.
Divisive Clustering on Dataset 2.

Figure 21.

Divisive Clustering on Dataset 3.
Divisive Clustering on Dataset 3.

Figure 22.

DBSCAN on Dataset 1.
DBSCAN on Dataset 1.

Figure 23.

DBSCAN on Dataset 2.
DBSCAN on Dataset 2.

Figure 24.

DBSCAN on Dataset 3.
DBSCAN on Dataset 3.

Figure 25.

OPTICS on Dataset 1.
OPTICS on Dataset 1.

Figure 26.

OPTICS on Dataset 2.
OPTICS on Dataset 2.

Figure 27.

OPTICS on Dataset 3.
OPTICS on Dataset 3.

Figure 28.

Spectral Clustering on Dataset 1.
Spectral Clustering on Dataset 1.

Figure 29.

Spectral Clustering on Dataset 2.
Spectral Clustering on Dataset 2.

Figure 30.

Spectral Clustering on Dataset 3.
Spectral Clustering on Dataset 3.

Figure 31.

K-means Clustering on Dataset 1.
K-means Clustering on Dataset 1.

Figure 32.

K-means Clustering on Dataset 2.
K-means Clustering on Dataset 2.

Figure 33.

K-means Clustering on Dataset 3.
K-means Clustering on Dataset 3.

Figure 34.

Average Silhouette Method on Dataset 1.
Average Silhouette Method on Dataset 1.

Figure 35.

Average Silhouette Method on Dataset 2.
Average Silhouette Method on Dataset 2.

Figure 36.

Average Silhouette Method on Dataset 3.
Average Silhouette Method on Dataset 3.

Figure 37.

Silhouette Method on Dataset 1.
Silhouette Method on Dataset 1.

Figure 38.

Silhouette Method on Dataset 2.
Silhouette Method on Dataset 2.

Figure 39.

Silhouette Method on Dataset 3.
Silhouette Method on Dataset 3.

Figure 40.

Calinski-Harabasz Index on Dataset 1.
Calinski-Harabasz Index on Dataset 1.

Figure 41.

Calinski-Harabasz Index on Dataset 2.
Calinski-Harabasz Index on Dataset 2.

Figure 42.

Calinski-Harabasz Index on Dataset 3.
Calinski-Harabasz Index on Dataset 3.

Figure 43.

K-means clustering with a predetermined number of clusters on Dataset 1.
K-means clustering with a predetermined number of clusters on Dataset 1.

Figure 44.

K-means clustering with a predetermined number of clusters on Dataset 2.
K-means clustering with a predetermined number of clusters on Dataset 2.

Figure 45.

K-means clustering with a predetermined number of clusters on Dataset 3.
K-means clustering with a predetermined number of clusters on Dataset 3.

Figure 46.

Elbow Method on Dataset 1.
Elbow Method on Dataset 1.

Figure 47.

Elbow Method on Dataset 2.
Elbow Method on Dataset 2.

Figure 48.

Elbow Method on Dataset 3.
Elbow Method on Dataset 3.

Figure 49.

Average Performance of Isolation Forest, One-Class SVM, Cluster-Based Method, Local Outlier Factor, and Autoencoders.
Average Performance of Isolation Forest, One-Class SVM, Cluster-Based Method, Local Outlier Factor, and Autoencoders.

Figure 50.

Average Performance of OPTICS, DBSCAN, Divisive Clustering, Spectral Clustering, K-means Clustering, and Agglomerative Clustering.
Average Performance of OPTICS, DBSCAN, Divisive Clustering, Spectral Clustering, K-means Clustering, and Agglomerative Clustering.

Figure 51.

Average Performance of the Average Silhouette, Silhouette Method, Calinski-Harabasz Index, K means clustering with a predetermined number of clusters and Elbow Method.
Average Performance of the Average Silhouette, Silhouette Method, Calinski-Harabasz Index, K means clustering with a predetermined number of clusters and Elbow Method.
DOI: https://doi.org/10.2478/ias-2024-0002 | Journal eISSN: 1554-1029 | Journal ISSN: 1554-1010
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Language: English
Page range: 15 - 35
Published on: Aug 31, 2024
Published by: Cerebration Science Publishing Co., Limited
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
Keywords:
CCTV surveillance,
home security,
rare pattern mining,
anomaly detection,
machine learning
Related subjects:
Computer sciences,
Fundamentals of computer sciences,
Theoretical computer sciences,
IT-security and cryptology

© 2024 Mintu Movi, Abdul Jabbar P, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.

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