Video surveillance: explosion detection

Lee, Shao Yuan (2025) Video surveillance: explosion detection. Final Year Project, UTAR.

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Abstract

The proliferation of surveillance technologies has emphasised their pivotal role in enhancing public safety by monitoring and detecting anomalies in real time. Among the anomalies, explosions present a grave threat due to the potentially resulting major loss of life, widespread panic and significant destruction of property. However, traditional surveillance systems are limited by their reliance on human monitoring, which is susceptible to oversight due to fatigue or distractions. Thus, this research focusses on developing an intelligent explosion detection surveillance system that is capable of early and accurate explosion detection. Explosions typically happen in a very short timeframe, often just a few seconds, leading to significant challenges for the rapid and accurate identification of explosions' unique visual patterns immediately. Hence, this study proposes to embed computer vision and advanced image processing algorithms, such as Motion History Images (MHI) and Motion Energy Images (MEI), into the intelligent explosion detection video surveillance system. By leveraging three motion-based variables, including motion ratio, new pixel ratio and optical flow values, together with three detection approaches, namely global detection, non-eroded detection and eroded detection, the system demonstrates the effectiveness of motion based methods in detecting explosions at an early stage with acceptable performance. Eventually, this approach aims to minimise explosions' damage by enabling immediate responses, preventing the spread of fires and the occurrence of secondary explosions.

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