Motion detection based on Gaussian mixture model and kernel distribution estimation
You Lv · Information technology newsletter · 2012
Background modeling and motion detection is an important step in video tracking.Non-parametric kernel density estimation(KDE) and Gaussian mixture model(GMM) are two classic methods in this field.This paper first introduces the basic principles of KDE and GMM,and analyses the advantages and disadvantages of both methods.Then a 2-step KDE-GMM cascade algorithm is proposed.It first does a fast segmentation of foreground and background region with KDE.For the region which cannot be modeled accurately,it uses GMM for a second judgment.The proposal method effectively combines their respective advantages.The test results showed that the improved algorithm is better than traditional ones and has good real-time and robustness.