Tracking and Modeling of Human Activity Using Laser Rangefinders

We describe a system that uses laser rangefinders to track the positions of people in typical environments and then builds predictive models of the observed movement patterns and interactions between persons. We represent all human activity as detected by the laser rangefinder system as a probability distribution over the space of possible displacements. The assumption is that different activities will map to distinct probability distributions. Position tracks are first segmented and clustered into short sequences representing different activities. The sequence of activity clusters is then used to build a stochastic model of the observed movement patterns and the typical frequency of occurrence. Interactions are assumed to occur between persons whose corresponding probability distributions exhibit a high degree of similarity. We describe the performance of the system on data recorded from unscripted activities that occurred in different environments: open layout laboratory, corridor, and an outdoor courtyard. In the laboratory environment, the system was able to detect interactions between people (ping-pong players) without utilizing a pre-defined model of specific interactions. In the courtyard environment, the system was able to flag a sudden increase in the number of people in the courtyard as an anomalous occurrence without any pre-defined concept of occupancy of the environment.