The proposed technique has been compared to the state of the art methods available in literature by using several videos of public and non-public dataset showing an improvement in the metrics. The algorithm provides also an alarm generator that can trigger an alarm signal if the smoke is persistent in a time window of 3 s. In the final step the convolutional neural network verifies the actual presence of smoke in the proposed regions of interest. This technique allows the automatic selection of specific regions of interest within the image by the generation of bounding boxes for gray colored moving objects. The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network. In this work presents a hybrid approach to assess the rapid and precise identification of smoke in a video sequence. Including intelligent video-based techniques in existing camera infrastructure enables faster response time if compared to traditional analog smoke detectors. Smoke detection represents a critical task for avoiding large scale fire disaster in industrial environment and cities.
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