Title | ||
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Exploiting R-CNN for video smoke/fire sensing in antifire surveillance indoor and outdoor systems for smart cities |
Abstract | ||
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This work presents a video-camera-based fire/smoke sensing technique for early warning in antifire surveillance systems. By exploiting R-CNN (Region Convolutional Neural Network), a detection technique is developed for the measurement of the smoke and fire characteristics in restricted video surveillance environments, both indoor (e.g. a railway carriage, container, bus wagon, homes, offices), or outdoor (e.g. storage or parking areas). The considered application scenario, to reduce costs, is composed of a single, fixed camera per scene, working in the visible spectral range already installed in a closed-circuit television system for surveillance purposes. The training phase is done with indoor and outdoor image sets, with both smoke and non-smoke scenarios to assess the capability of true-positive/true-negative detection and false-positive/false-negative rejection. To generate the training set, a Ground Truth Labeler app is used and applied to the open-access Firesense dataset, including tens of indoor and outdoor fire/ smoke scenes developed as the output of an FP7 project, plus other videos not publicly available, provided by Trenitalia during specific fire/smoke tests on railway wagons performed at their testing facility in Osmannoro, Italy. The achieved results show that the proposed R-CNN technique is suitable for the creation of a smart video-surveillance system for fire/smoke detection. |
Year | DOI | Venue |
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2020 | 10.1109/SMARTCOMP50058.2020.00083 | 2020 IEEE International Conference on Smart Computing (SMARTCOMP) |
Keywords | DocType | ISBN |
Video smoke/fire sensing,Ground Truth Labeler,R-CNN (Region Convolutional Neural Network),Smart surveillance | Conference | 978-1-7281-6998-9 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Sergio Saponara | 1 | 392 | 58.59 |
Abdussalam Elhanashi | 2 | 0 | 0.34 |
Alessio Gagliardi | 3 | 0 | 0.34 |