Detection and localisation of stationary objects by a pair of PTZ cameras

In this article we propose a novel approach for the detection and localisation of stationary objects using a pair of Pan-Tilt-Zoom (PTZ) cameras monitoring a wide scene. Our contribution is twofold. First we propose a stationary object detection and segmentation technique. It relies on the re-identification of foreground descriptors followed by a segmentation of these regions into objects, using Markov Random Fields. Our method allows the foreground to be dated and, under some conditions, to segment the different objects composing a single foreground blob. The second contribution concerns the matching of object silhouettes detected in each camera. This correspondence stage is only based on geometric constraints. Finally we tested our system on sequences which highlight its robustness to occlusions, even in the case of non planar scenes whose geometry is unknown.