Video surveillance: explosion detection
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 signi...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7112/1/fyp_CS_2025_LSH.pdf http://eprints.utar.edu.my/7112/ |
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| _version_ | 1854094476642877440 |
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| author | Lee, Shao Yuan |
| author_facet | Lee, Shao Yuan |
| author_sort | Lee, Shao Yuan |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7112 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.71122025-12-28T16:00:59Z Video surveillance: explosion detection Lee, Shao Yuan T Technology (General) 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. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7112/1/fyp_CS_2025_LSH.pdf Lee, Shao Yuan (2025) Video surveillance: explosion detection. Final Year Project, UTAR. http://eprints.utar.edu.my/7112/ |
| spellingShingle | T Technology (General) Lee, Shao Yuan Video surveillance: explosion detection |
| title | Video surveillance: explosion detection |
| title_full | Video surveillance: explosion detection |
| title_fullStr | Video surveillance: explosion detection |
| title_full_unstemmed | Video surveillance: explosion detection |
| title_short | Video surveillance: explosion detection |
| title_sort | video surveillance: explosion detection |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/7112/1/fyp_CS_2025_LSH.pdf http://eprints.utar.edu.my/7112/ |
| url_provider | http://eprints.utar.edu.my |