Towards More Efficient Use of Office Space

Abstract

Organizations that are constantly hiring new employees may run out of office space. Also, it is expensive to furnish and open new offices. Shared office space provides a cost-effective solution for big companies and startups. The main focus of our research is on understanding behavior patterns in work environments to increase workers' comfort and productivity. Specifically, the goal of this work is to detect habitual patterns of absence from a duty or obligation in order to provide a more efficient use of office space. In this paper, we propose to use a network of cheap low-resolution visual sensors (30 × 30 pixels) for long-term absenteeism detection of multiple persons in a work environment. Firstly, the users' locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users' mobility tracks, an occupancy map with the hotspot locations is detected. Finally, we propose an algorithm for detecting the absence patterns of each person. We evaluate our method on video sequences captured in a real work environment, where the persons' daily routines are recorded over five months. The results show that our approach of detecting the absence patterns achieves an accuracy of 97.70% in comparison to ground truth.