Video Smoke Detection Based on Accumulation and Main Motion Orientation
基于累积量和主运动方向的视频烟雾检测方法

Video smoke detection has many advantages over traditional methods,such as fast response,non-contact.But most of current methods for video smoke detection have high rates of false alarms.Through analyzing the characteristics of smoke motion,a novel video smoke detection is presented.In order to accelerate detection speed,video images are divided into blocks.Each block motion orientation is estimated by block matching methods.And a time sequence of motion orientation for each block is generated over a sliding time window.Then accumulation and main motion orientation are computed according to the sequence.The accumulation represents the degree of motion duration and the main motion orientation describes the maximum possible orientation of each block over the time window.A 3D feature is extracted from the accumulation and main motion orientation,and a Bayesian classifier is used for smoke detection.Experiments show that the algorithm is robust and significant for improving the accuracy of smoke detection.