Abstract:

To overcome the deficiencies of traditional fire smoke detection techniques and improve the detection rate of smoke detection algorithms, we propose a new smoke detection algorithm based on dense optical flow and edge features according to the characteristics of smoke movements. Firstly, the algorithm extracts the moving regions by combining the Gaussian mixture model (GMM) for background modeling with the frame difference method. Then by dividing the motion area into three parts, including the upper, middle and lower parts, the algorithm extracts optical flow vector features and edge orientation histograms from each part. Considering the continuous relevance of smoke movement in the time domain, the algorithm extracts the feature vectors of smoke from every three adjacent frames to enhance the robustness. Finally, the training and detection of smoke are implemented by using support vector machines. A high detection rate above 94% is obtained on the video test set. Experimental results show that the proposed algorithm can better adapt to complex environmental conditions in practical applications than other existing algorithms.

Key words: smoke detection, dense optical flow, edge orientation histogram, support vector machine