PhD forum: Multi-view occupancy maps using a network of low resolution visual sensors

Abstract

An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e.g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter using the occupancy map per time instance as measurement. We found that for a room of 8.8 by 9.2 m, 4 cameras with a resolution as low as 64 by 48 pixels was sufficient to estimate accurate occupancy maps and track up to 4 people. These findings indicate that it is possible to use low resolution visual sensors to build a cheap, power efficient and privacy-friendly system for occupancy monitoring.