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 activity maps captured from cameras of different perspectives to ones from a single orthographic perspective. The purpose of this is to be able to view and compare multiple activity maps from different cameras views over a wide area with consistently comparable data. A second contribution is that most mappings are based upon statistically learned camera perspectives, to minimize manual camera calibration. We demonstrate mapping results with multiple video datasets and describe applications for visualization and wide-area spatial probability estimation.