Video smoke recognition based on optical flow

A novel video smoke recognition method based on optical flow is presented. The result of optical flow is assumed to be an approximation of motion field. The method is proposed as following, first, moving pixels and regions in the video are determined by a background estimation method. Then, a pyramidal implementation of the Lucas Kanade feature tracker is proposed to calculate the optical flow of regions determined by the first step. And the average and variance of the corner points´ optical velocity are calculated which we call optical flow features and use to differentiate smoke from some other moving objects. Finally, examples consisting of features extracted from sequences of off-line videos are collected for the training of a discriminating model. A prototype of back-propagation neural networks is introduced for the discriminating model. Experiments show that the algorithm is significant for improving the accuracy of fire smoke detection and reducing false alarms.