Smoke, as a prominent character of combustion, is widely regarded as a signal of forest fire. Existing in a video-based smoke root detection methods on rely the distance between smoke and the lens, which is one of the most challenging parts. In relatively close distances, the dynamic region extraction method not only presents simplicity but also provides clear outlines and shapes, which is good for the smoke root extraction. However, when the distance increases, the advantage of this algorithm decreases and the rate of leak detection rises. To solve this challenge, this study developed a new algorithm which adopts Bayesian theory to combine the ViBe algorithm with the MSER algorithm. The likelihood functions are replaced by a small database, storing the descriptors of each frame and being updated in real time. The experiments demonstrate that the new method produces more complete shapes of candidate smoke root regions and lowers leak detection rate in full scale, compared with the ViBe results and MSER results, respectively. These improvements suggest that it can detect smoke in the forest more accurately.

烟是燃烧的主要特征，被广泛认为是森林火灾的信号。在基于视频的烟根检测方法中，依靠烟和镜头之间的距离是最有挑战性的部分之一。在相对较近的距离内，动​​态区域提取方法不仅呈现简单性，而且提供清晰的轮廓和形状，这对于烟根提取非常有用。但是，当距离增加时，该算法的优势会降低，并且泄漏检测率会上升。为了解决这一挑战，本研究开发了一种新的算法，该算法采用贝叶斯理论将ViBe算法与MSER算法结合在一起。可能性函数由一个小的数据库代替，该数据库存储每个帧的描述符并实时更新。实验表明，与ViBe结果和MSER结果相比，该新方法可产生更完整的候选烟根区域形状，并降低了全面的检漏率。这些改进表明它可以更准确地检测森林中的烟雾。