BoWFire：结合像素颜色和纹理分析的静止图像火灾检测
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
摘要：
涉及火灾的紧急事件具有潜在危害，需要快速准确的决策。在危机管理系统中使用众包图像和视频可以通过提供比口头/文本描述更多的信息来帮助解决这些问题。由于数据量通常很大，自动解决方案需要在不丢失相关信息的情况下丢弃不相关的内容。有几种使用基于颜色的模型在视频上进行火灾检测的方法。然而，它们不足以用于静止图像处理，因为它们可能遭受高的假阳性结果。这些方法还受到具有很少物理意义的参数的影响，这使得微调成为一项困难的任务。在这种情况下，我们提出了一种新的火灾检测方法，使用基于颜色特征的分类结合超像素区域的纹理分类的静止图像。我们的方法使用的参数数量减少，如果与以前的作品相比，简化了微调方法的过程。结果表明，我们的方法减少假阳性的有效性，而其精度保持与国家的最先进的方法兼容。
英文摘要：
Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.
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