Orthographic Perspective Mappings for Consistent Wide-Area Motion Feature Maps From Multiple Cameras

Spatiotemporal activity maps have been used to visualize where activity occurs over time, and are often displayed as pseudo-color heat maps. Our multi-dimensional activity map includes the following motion features: density, direction, bi-direction, velocity, and dwell. The primary contribution of this paper is to describe a set of mappings that will transform the activity maps captured from the cameras of different perspectives to the ones from a single orthographic perspective. The purpose of this is to be able to view and compare multiple activity maps from different camera views over a wide area with consistently comparable data. A second contribution is that the most mappings are based upon statistically learned camera perspectives to minimize manual camera calibration. We demonstrate mapping results with multiple video data sets and describe applications for visualization and wide-area spatial probability estimation.