Crowd flow estimation using multiple visual features for scenes with changing crowd densities

Crowd estimation and monitoring is an important surveillance task. We address the problem of estimating the “flow,” that is the number of persons passing a designated region in a unit time. We designate an area of the scene as a virtual trip wire and accumulate the total number of foreground pixels (in the trip wire) over a chosen time period. We show that cumulative pixel count is related to the number of persons passing through the trip-wire by a scale factor. This scale factor is highly sensitive to the “crowdedness” (levels of crowd density) of the scene which creates different levels of occlusion of the individuals walking/passing through the trip-wire. We use texture features to determine the crowdedness and choose the most appropriate scaling factor. Our method does not require detection and tracking of individuals and is robust to scene dynamics, background subtraction errors, and different crowd levels.