Design and Development of Cross Capture Cam (3C): A Disaster and Traffic Management and Monitoring System using Image Detection of Urdaneta City

Abstract

Purpose  – The study aims to develop an innovative approach for monitoring traffic and disaster incidents in the Public and Safety Office of Urdaneta City. This involves implementing image-based processing algorithms through strategically positioned CCTV cameras on city streets. Specific research objectives include identifying user requirements for the monitoring system, determining the suitable image-processing framework, and assessing the acceptance of the developed system.

Method – The research primarily focuses on designing and developing an image processing-based traffic and disaster monitoring system. Adopting Extreme Programming (XP) as the software development methodology, the researchers prioritize rapid deliverable production and a collaborative environment between developers and clients. The study employs a descriptive research approach, utilizing quantitative analyses for data collection. Various instruments, such as interviews, survey questionnaires, observations, and literature reviews, were employed to gather user requirements and feedback. ISO 9126 was utilized for assessing user acceptability, offering a structured approach to evaluating software quality.

Results – The study aimed to streamline traffic management for the management team by developing a digital system. The focus was on the role of CCTV cameras in reducing crime and traffic violations. Findings highlighted the effectiveness of CCTV installations, particularly at red lights and intersections. Interviews with the Public Order and Safety Office in Urdaneta City emphasized the challenges in manual monitoring and the importance of adhering to safety rules. Collaboration with the PNP Urdaneta highlighted the need for timely responses to incidents. The study underscores the role of technology, collaboration, and efficient reporting in enhancing traffic management and public safety.

Conclusion – In this study, our focus was on creating an image recognition-based traffic and incident monitoring system utilizing video surveillance cameras for implementation in Urdaneta City. The following conclusions have been derived: The project requirements were meticulously analyzed by examining the existing business rules and policies of POSO Urdaneta City in incident monitoring implementation, influencing the design and development of the proposed system. While YOLOv3 proved efficient with its AI-based features for achieving research goals, its resource-intensive nature and limited small object detection capacity suggest considering alternative versions for enhanced performance in similar algorithm development. User acceptability testing results reveal a high acceptance level (GWM of 4.5), signifying satisfaction among system implementers. However, the researchers recommend additional technical testing on the CCTV devices for further refinement.

Recommendations – The research work has provided means of traffic monitoring through the use of technological innovations. Thus, to support the successful implementation of these technologies, the organization should maintain a sufficient working environment for these tools.

Research Implications – This undertaking provides insights as an administrative strategy to enhance traffic management and monitoring procedures using image-based detection.  This study can be used to minimize errors and provide comprehensive and evidence-based documentation for traffic and disaster management that will be used in the future.

Social Implications  –  This research endeavor aimed to be part of the mechanism to provide a safer and more secure environment for the community enhancing their safety and security.