Fast motion detection based on accumulative optical flow and double background model

Authors:
Jin Zheng;Bo Li;Bing Zhou;Wei Li
Affiliations:
Digital Media Lab, School of Computer Science & Engineering, Beihang University, Beijing, China;Digital Media Lab, School of Computer Science & Engineering, Beihang University, Beijing, China;Digital Media Lab, School of Computer Science & Engineering, Beihang University, Beijing, China;Digital Media Lab, School of Computer Science & Engineering, Beihang University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Year:
2005

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Abstract

Optical flow and background subtraction are important methods for detecting motion in video sequences. This paper integrates the advantages of these two methods. Firstly, proposes a high precise algorithm for optical flow computation with analytic wavelet and M-estimator to solve the optical flow restricted equations. Secondly, introduces the extended accumulative optical flow and also provides its computational strategies, then obtains a robust motion detection algorithm. Furthermore, combines a background subtraction algorithm based on the double background model with the extended accumulative optical flow to give an abnormity alarm in time. All obvious proofs of experiments show that, our algorithm can precisely detect moving objects, no matter slow or little, preferably solve the occlusions as well as give an alarm fast.