Gaussian mixture model is a popular method to model dynamic scenes viewed by a fixed camera. However, we found
that it is not a trivial problem for each component of Gaussian mixture to learn the accurate parameters for complex
pixels. Furthermore, traditional method of Gaussian mixture has to make a tradeoff between system stability and
convergence rate. We developed a mechanism of double-layer Gaussian mixture model for moving object detection from
dynamic scenes, which can improve the convergence rate without compromising the system stability. Additionally,
temporal consistency of variances was taken into account to alleviate camouflage problems in the process of detection.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.