目的 视频烟雾检测具有响应速度快、不易受环境因素影响、适用面广、成本低等优势，为及早预警火灾提供有力保障。近年涌现大量视频检测方法，尽管检测率有所提升，但仍受到高误报率和高漏报率的困扰。为了全面反映视频烟雾检测的研究现状和最新进展，本文重点针对2014年至2017年国内外公开发表的主要文献，进行全面的梳理和分析。方法 该工作建立在广泛文献调研的基础上，立足于视频烟雾检测的基本框架，围绕视频图像预处理、疑似烟区提取、烟雾特征描述、烟雾分类识别等处理阶段，系统地对最新文献进行分析和总结。此外，对区别于传统框架的深度学习检测方法亦进行了相关归纳。结果 重点依据烟雾运动特征和烟雾静态特征这两类，对疑似烟区提取方法进行梳理；从统计量特征、变换域特征和局部模式特征3个方面对烟雾特征描述方法进行梳理，并从颜色、形状等七个角度进行总结；从基于规则和基于学习这两个视角，梳理烟雾识别和决策方法；最后，对于基于深度学习的方法单独进行了阐述。文献通过系统地梳理，凝练出视频烟雾检测近几年取得的进展和尚存在的不足，并对视频烟雾检测发展前景进行展望。结论 针对视频烟雾检测的研究一直备受青睐，越来越多性能优秀的检测算法不断涌现。通过对现有研究进行全面梳理和系统分析，期望视频烟雾检测能取得更大的进展并更好地应用于工业领域，为火灾预警提供更有力的保障。

Objective Video smoke detection methods can guarantee real-time fire alarms because these methods respond quickly to fire and have strong robustness to the environment, suitability for various scenes, and low-cost application. Many state-of-the-art video smoke detection methods have been proposed recently. The detection rates of these methods have been greatly improved by recent efforts, but these methods still suffer from the problem of high false and missing alarms. We provide an up-to-date critical survey of research on video smoke detection methods to keep up with the latest research progress, research focus, and development trends in video smoke detection. We focused on domestic and international research on video smoke detection published from 2014 to 2017. These publications include feature extraction, smoke recognition, and detection based on images and videos. Method We review papers on video smoke detection and summarize a general research framework for video smoke detection. The general framework of video smoke detection indicates that the general procedure of these methods is divided into several processing steps, namely, video preprocessing, detection of candidate smoke regions, feature extraction of smoke regions, video smoke classification, and other processing techniques. We discuss these methods in detail according to the general processing steps. Aside from describing traditional smoke detection methods based on handcrafted features, we also discuss and analyze deep learning-based smoke detection methods that were recently proposed given that deep learning is a hot area in machine learning research. Result On the basis of the general processing steps, we analyze these video preprocessing methods and divide the relevant literature into three major categories. These video preprocessing methods include preprocessing methods for color, preprocessing techniques for noise interference, and preprocessing approaches to image segmentation. Candidate smoke regions are detected in two ways. One way is to simply divide the image into a set of blocks, and each image block is tested. Some blocks may be classified as a candidate smoke region. Another way is to extract complete candidate smoke regions. The extraction methods of complete candidate smoke regions are sorted according to smoke motion and static characteristics. The information of smoke motion features can be obtained through object detection based on background modeling technology and object detection based on the simple differences of adjacent frames.Some static characteristics of smoke can be summed up as traditional features, such as color and shape. Other static characteristics are extracted by methods of a novel perspective. Statistical measures, transformation domain, and local features are classified into seven categories of features:color, shape, gradient, orientation, textures, frequency, and motion. Classification methods for video smoke detection are first reviewed and then categorized into two types, namely, rule-based methods and learning-based ones. Deep learning-based methods are reviewed independently because they are different from traditional smoke recognition methods. On the basis of the above analysis of these methods, we detail the advantages of existing video smoke detection methods. State-of-the-art methods with high detection rates and low false alarm rates have been proposed in recent years. Some novel methods exist. Some methods attempt to explore classification methods. Some datasets have been created and widely used to facilitate the training and testing of methods. We also elaborate the shortcomings of existing video smoke detection methods. The false alarm rates and error rates of detection methods remain high. Most algorithms are dependent on scenes and are easily disturbed by noise. Smoke features are simply combined without rules in some methods. Moreover, no publicly available video datasets exist with labeled smoke regions and standard evaluation criteria for video smoke detection. Finally, we suggest possible promising directions for future research. First, a set of video datasets is manually labeled, and standard evaluation criteria must be established. Second, researchers must explore essential features of smoke. Third, an effective fusing method for multiple smoke features extracted by different methods is needed. Fourth, smoke features must be automatically learned by machine learning methods instead of handcrafted designed ones. Finally, we refer to a new detection framework, such as deep learning-based frameworks, which are completely different from the basic framework. Conclusion Video smoke detection methods are one of the most important and popular research topics nowadays. Our review and analysis of existing methods may provide researchers with powerful support for their work on early fire alarms, promote the advancement of video smoke detection, and further push industrial applications for video smoke detection.