Monthly Archives: February 2016

Motion Detection : Mean Background Model

The simplest technique is to take a background image without any moving object. Subtract current frame from static background image to find changes in current frame. But it can’t deal with objects being added or removed from static scene, illumination changes and changes in camera position etc. To overcome above mentioned issues frame difference approach… Read More »

Motion Detection using Frame Differencing

Instead of using static background model to find difference in current frame, I am using frame difference technique where current frame is compared or subtracted from previous frame. The difference depends upon speed of moving objects. Making a fair analysis is very difficult. Find the code bellow to find difference in frames.

Frame Difference

Averaging Specified Number of Frame in OpenCV

The following OpenCV code helps you to find average frame model for background subtraction purpose. This code is tested on EWAP pedestrian data set. An average frame is extracted and upload below.