The smoke detection technology plays an important role in preventing early fire spread. An accurate and fast smoke detection algorithm has very important practical application value. In recent years, with the rapid development of machine vision and image processing technology, smoke detection algorithms for video-oriented images have attracted extensive attention due to their non-contact and strong robustness. The smoke detection algorithm based on video images can effectively overcome the deficiency of traditional smoke detectors working close to fire sources. However, the smoke detection algorithm based on video images still faces great challenges due to the complexity of scenes and the uncertainty of environmental factors. First, the basic process of the smoke detection technology is briefly introduced, including pretreatment, feature extraction, and classification recognition. Second, the preprocessing method based on color and motion segmentation is introduced, and the visual characteristics and movement characteristics of smoke are further analyzed and the related smoke extraction algorithms are introduced. Third, some of the current commonly used smoke detection classifiers and the deep learning network models are discussed and summarized. Finally, the deficiencies of the smoke detection algorithm are mainly introduced and the future of the smoke detection algorithm is prospected.