Abstract
In recent years guider-follower approaches show a promising solution to the challenging problem of last-mile or indoor pedestrian navigation without micro-maps or indoor floor plans for path planning. However, the success of such guider-follower approaches is highly dependent on a set of manually and carefully chosen image or video checkpoints. This selection process is tedious and error-prone. To address this issue, we first conduct a pilot study to understand how users as guiders select critical checkpoints from a video recorded while walking along a route, leading to a set of criteria for automatic checkpoint selection. By using these criteria, including visibility, stairs and clearness, we then implement this automation process. The key behind our technique is a lightweight, effective algorithm using left-hand-side and right-hand-side objects for path occlusion detection, which benefits both automatic checkpoint selection and occlusion-aware path annotation on selected image checkpoints. Our experimental results show that our automatic checkpoint selection method works well in different navigation scenarios. The quality of automatically selected checkpoints is comparable to that of manually selected ones and higher than that of checkpoints by alternative automatic methods.