Probabilistic human mobility model in indoor environment
Document Type
Conference Proceeding
Date of Original Version
10-31-2016
Abstract
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.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Volume
2016-October
Citation/Publisher Attribution
Tang, Bo, Chao Jiang, Haibo He, and Yi Guo. "Probabilistic human mobility model in indoor environment." Proceedings of the International Joint Conference on Neural Networks 2016-October, (2016): 1601-1608. doi: 10.1109/IJCNN.2016.7727389.