%0 Journal Article
	%A Anton Stadler and  Thorsten Ike
	%D 2016
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 117, 2016
	%T Real Time Video Based Smoke Detection Using Double Optical Flow Estimation
	%U https://publications.waset.org/pdf/10005541
	%V 117
	%X In this paper, we present a video based smoke detection
algorithm based on TVL1 optical flow estimation. The main part
of the algorithm is an accumulating system for motion angles and
upward motion speed of the flow field. We optimized the usage of
TVL1 flow estimation for the detection of smoke with very low smoke
density. Therefore, we use adapted flow parameters and estimate the
flow field on difference images. We show in theory and in evaluation
that this improves the performance of smoke detection significantly.
We evaluate the smoke algorithm using videos with different smoke
densities and different backgrounds. We show that smoke detection
is very reliable in varying scenarios. Further we verify that our
algorithm is very robust towards crowded scenes disturbance videos.
	%P 1660 - 1665