Geometry-Based Multiple Camera Head Detection in Dense Crowds
This addresses the problem of pedestrian monitoring in crowded environments for surveillance applications, but it is incremental as it builds on existing geometric methods.
The paper tackles head detection in dense crowds by using geometric consistency across multiple cameras without learning, achieving detection of tens of heavily occluded pedestrians in an outdoor setting with only three views.
This paper addresses the problem of head detection in crowded environments. Our detection is based entirely on the geometric consistency across cameras with overlapping fields of view, and no additional learning process is required. We propose a fully unsupervised method for inferring scene and camera geometry, in contrast to existing algorithms which require specific calibration procedures. Moreover, we avoid relying on the presence of body parts other than heads or on background subtraction, which have limited effectiveness under heavy clutter. We cast the head detection problem as a stereo MRF-based optimization of a dense pedestrian height map, and we introduce a constraint which aligns the height gradient according to the vertical vanishing point direction. We validate the method in an outdoor setting with varying pedestrian density levels. With only three views, our approach is able to detect simultaneously tens of heavily occluded pedestrians across a large, homogeneous area.