Discovering activity patterns in office environment using a network of low-resolution visual sensors

Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events

Towards more efficient use of office space

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. Specially, 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 (30x30 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 recusive 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