An AR Benchmark System for Indoor Planar Object Tracking

Planar object tracking (POT) is the basis of many indoor AR applications. However, there still lacks a systematic way to assess AR trackers. Existing benchmarks usually focus on the tracking accuracy of an algorithm without sufficient details about its sensitivity to various object properties and user behaviors, shedding limited light on possible resulting usability issues. We therefore propose a comprehensive POT benchmark system to understand the weakness of a tracker and derive cues for system improvement. We first identify a set of objects that are commonly used as indoor mobile AR markers and specify their vision-related properties. We then construct a video collection to record typical user interactions with these markers, and statistically quantify the consequent changes as a result of individual or multiple basic manipulations. Evaluation shows that this work can expose a tracker's sensitiveness to different object properties and user behaviors, drawing insights for system improvement and algorithm design.