Comprehensive Anomaly Detection in Ghana: Addressing Social, Economic, and Security Challenges Through Transfer Learning in Traffic Surveillance

Volume 6, Issue 1, February 2024     |     PP. 1-22      |     PDF (2506 K)    |     Pub. Date: March 5, 2024
DOI: 10.54647/tte580069    33 Downloads     154054 Views  

Author(s)

Isaac Opei, Regentropfen College of Applied Sciences, Ghana
Isaac Azure, Regentropfen College of Applied Sciences, Ghana
Anas Musah, Regentropfen College of Applied Sciences, Ghana

Abstract
The abstract presents a comprehensive overview of the social and economic challenges faced by Ghanaians, including high inflation, currency depreciation, and a surge in violent crimes such as armed robberies and murders. It highlights the alarming increase in road accidents, emphasizing their significant impact on mortality rates and the country's GDP. The document expresses concern about the poor response time of security agencies to these anomalies, leading to reputational consequences and a potential deterrent for foreign investors. The Inspector General of Police aims to reduce response time, but existing solutions focus on specific types of anomalies. The abstract proposes using transfer learning, specifically the DenseNet121 method, to detect various anomalies in traffic surveillance videos. The study details the training process, dataset augmentation, and model evaluation. While the proposed model shows positive results in identifying anomalies in certain conditions, the abstract suggests the need for improved models to handle long-distance, poor coverage, and hazy environment scenarios in future research.

Keywords
Ghana, Anomalies, Transfer learning, DenseNet121, Security agencies, Response time

Cite this paper
Isaac Opei, Isaac Azure, Anas Musah, Comprehensive Anomaly Detection in Ghana: Addressing Social, Economic, and Security Challenges Through Transfer Learning in Traffic Surveillance , SCIREA Journal of Traffic and Transportation. Volume 6, Issue 1, February 2024 | PP. 1-22. 10.54647/tte580069

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