  We present the SAMMI lightweight object detection method which has a high
level of accuracy and robustness, and which is able to operate in an
environment with a large number of cameras. Background modeling is based on DCT
coefficients provided by cameras. Foreground detection uses similarity in
temporal characteristics of adjacent blocks of pixels, which is a
computationally inexpensive way to make use of object coherence. Scene model
updating uses the approximated median method for improved performance.
Evaluation at pixel level and application level shows that SAMMI object
detection performs better and faster than the conventional Mixture of Gaussians
method.
