Law enforcement officials rely heavily on Forensic Video Analytic (FVA) Software in their investigation of crimes. However, the current FVA software available in the market is complex, time-consuming, equipment-dependent, and expensive. This poses a problem for developing countries that struggle to gain access to this crucial technology.

To address these shortcomings, a team embarked on a Final Year Project to develop an efficient and effective FVA software. They conducted a thorough review of scholarly research papers, online databases, and legal documentation to identify the areas that needed improvement. The scope of their project covered multiple aspects of FVA, including object detection, object tracking, anomaly detection, activity recognition, tampering detection, image enhancement, and video synopsis.

To achieve their goals, the team employed various machine learning techniques, GPU acceleration, and efficient architecture development. They used CNN, GMM, multithreading, and OpenCV C++ coding to create their software. By implementing these methods, they aimed to speed up the FVA process, particularly through their innovative research on video synopsis.

The project yielded three significant research outcomes: Moving Object Based Collision-Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture, and Tampering Detection Inter-Frame Forgery. These outcomes were achieved through the integration of efficient algorithms and optimizations to overcome limitations in processing power and memory. The team had to strike a balance between real-time performance and accuracy to ensure the software’s practicality.

Additionally, the research outcomes included forensic and surveillance panel outcomes specifically tailored for the Sri Lankan context. This demonstrates the team’s focus on addressing the needs and challenges faced by law enforcement in their home country.

In conclusion, this Final Year Project successfully developed an efficient and effective FVA software by leveraging machine learning techniques, optimized algorithms, and innovative research on video synopsis. The implications of their work are far-reaching, potentially revolutionizing the way law enforcement agencies handle video evidence. This project serves as a stepping stone towards providing developing countries with better access to the tools and technology needed for effective crime investigation and prevention.
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