iPAC: Integrate Pedestrian Dead Reckoning and Computer Vision for Indoor Localization and Tracking

Indoor localization is of great importance in the era of mobile computing. Smartphone-based pedestrian tracking is essential to a wide range of applications in shopping malls, industries, office buildings, and other public places. Current mainstream solutions rely on radio fingerprints and/or inertial sensors to distinguish and track pedestrians. However, these methods suffer from considerable deployment efforts and large accumulative errors. In recent years, the increasing numbers of security surveillance cameras installed in public areas provide a fresh perspective to overcome these drawbacks. However, in the real dynamic environments, fusing camera-based and inertial sensors-based pedestrian tracking is non-trivial due to the low robustness of visual tracking, incorrespondence of identifications and high complexity of computation. This paper presents the design and implementation of iPAC, an integrated inertial sensor and camera-based indoor localization and tracking system that achieves high accuracy in dynamic indoor environments with zero human effort. iPAC designs a robust visual detection and tracking algorithm to differentiate and track pedestrians in dynamic environments. Furthermore, iPAC employs a motion sequence-based matching algorithm to fuse raw estimates from both systems. By doing so, iPAC outputs enhanced accuracy, while overcoming the respective drawbacks of each sub-system. We implement iPAC on commodity smartphones and validate its performance in complex environments (including a laboratory, a classroom building, and a office building). The result shows that iPAC achieves a remarkable detection success rate of 93% and tracking success rate of 95% even suffering from severe line-of-sight blockages.