Smoke detection with no latency for fire alarming is crucial to minimize fire damages and saving lives. Video as a spatio-temporal sensor covers a larger area than point sensors and it is sensitive to environment changes. Current smoke detection algorithms

however

are still difficult to achieve fast

accurate

and robust judgment on fire

even though they have used chromatic characters

texture

shape

flutter

flicker

spatial and temporal frequencies

as well as composite classifiers such as support vector machine

neural network

etc. This survey reviews the state-of-art of smoke detection methods and proposes three directions to achieve robust data processing. We argue that the success of visual smoke detection should be consolidated by a rigorous understanding of its physical characteristics

creation of a common test database for algorithm validation and comparison

and the establishment of a new criteria for the evaluation of algorithms.