A fast accumulative motion orientation model based on integral image for video smoke detection
Video smoke detection has many advantages over traditional methods, such as fast response, non-contact, and so on. But most of video smoke detection systems usually have high false alarms. In order to improve the performance of video smoke detection, we propose an accumulative motion model based on the integral image by fast estimating the motion orientation of smoke. But the estimation is not very precise due to block sum. Not very accurate estimation will affect the subsequent decision. To reduce this influence, the accumulation of the orientation over time is performed to compensate results for the inaccuracy of orientation. The model is able to mostly eliminate the disturbance of artificial lights and non-smoke moving objects by using the accumulation of motion. The model together with chrominance detection can correctly detect the existence of smoke. Experimental results show that our algorithm has good robustness for smoke detection.
video smoke detection computer vision fire detection motion estimation orientation accumulation