Human detection using multi-camera and 3D scene knowledge

Human detection has attracted much attention in recent years due to its widespread applications. Most existing multi-camera systems focus on locating moving people in each camera and thus resolve the occlusion, which cannot detect still people in images. To overcome this problem, we extend previous 3D search method from single camera to multi-camera. We first use the method of multiple view geometry to construct the 3D search grid. Each grid point on the 3D ground plane is represented by a cylinder. Then, we re-project these cylinders to each view and classify the re-projected sub-images. Finally, by fusing the detected results from each camera, we can detect still people accurately and handle occlusion effectively. Experiments show that our method is comparable to state-of-the-art techniques on challenging datasets, without assuming that people are moving.