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A real-time video smoke detection algorithm based on Kalman filter and CNN
Journal of Real-Time Image Processing ( IF 3 ) Pub Date : 2021-03-19 , DOI: 10.1007/s11554-021-01094-y
Alessio Gagliardi , Francesco de Gioia , Sergio Saponara

Smoke detection represents a critical task for avoiding large scale fire disaster in industrial environment and cities. Including intelligent video-based techniques in existing camera infrastructure enables faster response time if compared to traditional analog smoke detectors. In this work presents a hybrid approach to assess the rapid and precise identification of smoke in a video sequence. The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network. 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. In the final step the convolutional neural network verifies the actual presence of smoke in the proposed regions of interest. 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. 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. Finally, we developed a portable solution for embedded systems and evaluated its performance for the Raspberry Pi 3 and the Nvidia Jetson Nano.



中文翻译:

基于卡尔曼滤波器和CNN的实时视频烟雾探测算法

烟雾检测是避免工业环境和城市发生大规模火灾的关键任务。与传统的模拟烟雾探测器相比,在现有的相机基础设施中包括基于智能视频的技术可加快响应时间。在这项工作中,提出了一种混合方法来评估视频序列中烟雾的快速和精确识别。该算法结合了基于卡尔曼滤波和运动检测的传统特征检测器以及轻量级浅层卷积神经网络。该技术通过生成灰色运动对象的边界框,可以自动选择图像中特定的特定关注区域。在最后一步中,卷积神经网络验证所提议的感兴趣区域中烟雾的实际存在。该算法还提供了警报生成器,如果烟雾在3 s的时间窗口内持续存在,则可以触发警报信号。通过使用显示度量标准有所改进的几个公共和非公共数据集的视频,已将拟议的技术与文献中现有的现有技术方法进行了比较。最后,我们为嵌入式系统开发了便携式解决方案,并评估了其在Raspberry Pi 3和Nvidia Jetson Nano上的性能。

更新日期:2021-03-19
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