There has been an increase in the use of video surveillance and monitoring in public areas to improve safety and security. Change detection and crowding/congestion density estimation are two sub-tasks in a subway monitoring system. We propose a method that decomposes this problem into two steps. The first step consists of a change detection algorithm that distinguishes the background from the foreground. This is done using a discontinuity preserving MRF-based approach where the information from different sources (background subtraction, intensity modeling) is combined with spatial constraints to provide a smooth motion detection map. Then, the obtained change detection map is combined with a geometry module that performs a soft auto-calibration to estimate a measure of congestion of the observed area (platform). Extensive experimental results in a metro station of a metropolitan city demonstrates the performance and the potential of our method.