Robust, Fine-Grained Occupancy Estimation via Combined Camera & WiFi Indoor Localization

Abstract

We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types of human views: {head, torso, full-body}) to location coordinates. We develop statistical techniques to overcome these practical challenges, and finally propose a novel fusion algorithm, based on inexact bipartite matching of these two streams of independent estimates, to estimate the occupancy in complex, multi-inhabitant indoor spaces (such as university labs). We experimentally demonstrate that this estimation technique is robust and accurate, achieving less than 20% error, in an approx. 85m2 lab space (with the error staying below 30% in a smaller 25m2 area), across a wide variety of occupancy conditions.