Already a member? Log in

Sign up with your...

or

Sign Up with your email address

Add Tags

Duplicate Tags

Rename Tags

Share This URL With Others!

Save Link

Sign in

Sign Up with your email address

Sign up

By clicking the button, you agree to the Terms & Conditions.

Forgot Password?

Please enter your username below and press the send button.
A password reset link will be sent to you.

If you are unable to access the email address originally associated with your Delicious account, we recommend creating a new account.

URL: http://bmc.univ-bpclermont.fr/

Background Models Challenge (BMC)

Many real-time vision-based applications devoted to video surveillance search for distinguishing moving objects from an image sequence given by a static camera. This background/foreground segmentation stage could be addressed by simple approaches, e.g. by computing the difference between two successive frames, or by building a time-averaged background image. However, such simple algorithms are very limited in outdoor environment for instance (because of global variation of luminance, shadows of objects,etc.).

Since this is an important step in video analysis, many background subtraction algorithms have been proposed since 90’s to tackle these problems. Although the evaluation of BSA is an important issue, relevant papers that handle with both benchmarks and annotated dataset are limited [2,3]. Moreover, many authors that propose a novel approach use [1] as a gold-standard, but rarely compare their method with recent related work. BMC is thus an opportunity for researchers at both universities and companies to evaluate the quality of their work.

References

[1] C. Stauffer and W. E. L. Grimson. Adaptative background mixture models for a real-time tracking. In International Conference on Computer Vision and Pattern Recognition, 1999.

[2] Y. Benezeth, P-M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger. Review and evaluation of commonly-implemented background subtraction algorithms. In International Conference on Pattern Recognition, 2008.

[3] A. Prati, I. Mikic, M. Trivedi, and R. Cucchiara. Detecting moving shadows: Algorithms and evaluation. In IEEE Trans. PAMI, 2003.

Share It With Others!