Parameter-unaware autocalibration for occupancy mapping

People localization and occupancy mapping are common and important tasks for multi-camera systems. In this paper, we present a novel approach to overcome the hurdle of manual extrinsic calibration of the multi-camera system. Our approach is completely parameter unaware, meaning that the user does not need to know the focal length, position or viewing angle in advance, nor will these values be calibrated as such. The only requirement to the multi-camera setup is that the views overlap substantially and are mounted at approximately the same height, requirements that are satisfied in most typical multi-camera configurations. The proposed method uses the observed height of an object or person moving through the space to estimate the distance to the object or person. Using this distance to backproject the lowest point of each detected object, we obtain a rotated and anisotropically scaled view of the ground plane for each camera. An algorithm is presented to estimate the anisotropic scaling parameters and rotation for each camera, after which ground plane positions can be computed up to an isotropic scale factor. Lens distortion is not taken into account. The method is tested in simulation yielding average accuracies within 5cm, and in a real multi-camera environment with an accuracy within 15cm

Learning about objects in the meeting rooms from people trajectories

In ambient intelligence object recognition is an important step towards behaviour analysis and the understanding interactions between people and the environment. Existing methods focus on a detailed analysis of image content using colour, shape, texture and motion analysis (direct recognition). In this paper we present a method for recognizing furniture, i.e. chairs, tables and the walking area in a meeting room using the estimated trajectories of people (indirect recognition). We use Support Vector Machines (SVMs) to classify the activities into three categories: sitting, standing and walking to create two occupancy maps for sitting and walking spaces according to Bayesian theory. The positions of the chairs and tables are inferred from these maps. We compared the recognition of chairs and tables to ground truth data on meeting scenarios. The performance of this method is good