TY - JFULL AU - Anton Stadler and Thorsten Ike PY - 2016/10/ TI - Real Time Video Based Smoke Detection Using Double Optical Flow Estimation T2 - International Journal of Computer and Information Engineering SP - 1659 EP - 1665 VL - 10 SN - 1307-6892 UR - https://publications.waset.org/pdf/10005541 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 117, 2016 N2 - 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. ER -