Probabilistic Human Mobility Model in Indoor Environment
This work addresses the need for prior knowledge of human distribution to improve robot-assisted activities, but it is incremental as it builds on existing probabilistic frameworks.
The paper tackles the problem of modeling human mobility in indoor environments by proposing a probabilistic method that incorporates internal and external factors, and validates it using real surveillance and survey data from a shopping mall and a college dorm.
Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.