In this paper, we present an approach for multicamera pedestrian detection exploiting the concepts of multiview geometry and the shapes of 3D geometric primitives. Multicamera occupancy maps provide peak responses corresponding to the object detection but suffer from several false detections known as ghosts. The novelty of this paper is the introduction of shape patterns which can model the objects, such as pedestrians, by defining a kernel function in the projected occupancy space. This kernel depends upon the geometry of the 3D primitives and also varies in relation to their position with respect to the cameras in the real world configuration. For multiple objects visible across several cameras, we define a formation model which is the convolution of this spatially varying kernel with the set of possible object locations. The locations corresponding to detections can thus be obtained through a deconvolution process. For efficient computations, we further propose an estimated deconvolution process specific to our kernel responses which can also be heavily parallelized. We show the application of this process towards pedestrian detection by studying various 3D cylindrical primitives. Experiments on two public dataset sequences, including comparison with another approach, show the efficiency of the proposed method in terms of pedestrian detection and ghost pruning, including in adverse and challenging conditions.