Economic and Synergistic Pedestrian Tracking System with Service Cooperation for Indoor Environments

Introduction

Tracking technology, which measures the position and orientation of a human, is one of the fundamental technologies for realizing location-aware services and for analyzing human behaviors induced by provided services by an objective indicator. Previously, we have proposed an indoor/outdoor pedestrian tracking system (Kourogi et al., 2006) based on Pedestrian Dead-Reckoning (PDR) using wearable self-contained sensors for realizing location-aware services such as pedestrian navigation, and improvement of quality of services through analysis of pedestrian traffic and actions. We have also developed a museum guide system (Okuma et al., 2007) that presents recommendation routes and content based on the position and orientation of the user from the tracking system in a large indoor environment (four floors, 2,500 to 2,700m² each). Over the past three years, we have carried out practical experiments and user studies of the museum guide and tracking systems in the actual field and have been improving the systems based on feedback from users who operated the systems in the field.

For a navigation service, the service providers first create and place maps and annotations as content before operating the service, and then the navigation system presents them based on the position and orientation of the users. After the operation of the navigation, the service providers consider how to improve the service from subjective evaluations of the users by questionnaires, interviews, and action analysis of the users based on the tracking logs. In each step of the service cycle, efficiently creating content, realizing good user experience and effectively analyzing user’s actions have been desired. Actually, they and Ishikawa et al. (2009) have promoted efficiently providing the navigation service and have improved user experience based on feedback from the users by developing an 3-D indoor modeling system which uses the pedestrian tracking system for efficiently creating maps and a lightweight client using iPhone 3G (Figure 1) for displaying maps and content to the user.

Figure 1.

Museum guide system using self-contained sensors, sparse infrastructures, and iPhone 3G

However, there are still many issues. In particular, a calibration of human walking parameters for our pedestrian tracking system is a significant factor disturbing good user experience. The tracking system has to calibrate user’s walking parameters which can be used to estimate walking velocity corresponding to the walking locomotion for estimating precise location of the user before using the system. For the calibration, the user has to walk several times at various walking speeds, and it is burdensome to the user. Moreover, the parameters that are estimated in advance are not necessarily effective because the walking parameters change based on user activity. Therefore, it is necessary to estimate the walking parameters according to the present condition of the user without burdening the user. Furthermore, the costs for introduction and improvement of the pedestrian tracking system have to be low and reasonable in order to apply the system to actual service fields. The cost problems for operating services are well considered not only for the museum guide service but also for the other services.

We propose the following approaches for improving the tracking accuracy of the pedestrian tracking service and addressing the cost problem for the improvement.

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  Dynamic walking parameter estimation and error correction of pedestrian tracking by utilizing existing infrastructure such as surveillance cameras and active RFID tags

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  Enhancement of the quality and value of services by incorporating with other services, and reduction of overall introduction and running costs for the services

For estimating walking parameters of the user of the tracking service, our tracking system estimates the parameters dynamically by using surveillance cameras, which is used for security services while the user uses the tracking system. In addition, our system also corrects errors of position and orientation by using the surveillance cameras and RFID tags. Using existing infrastructures, the tracking system can improve the tracking accuracy without additional introduction and running costs for the infrastructures and burdening the user.