This paper aims at accurate and efficient pose tracking of planar targets on modern smartphones. Existing methods, relying on either visual features or motion sensing based on built-in inertial sensors, are either too computationally expensive to achieve realtime performance on a smartphone, or too noisy to achieve sufficient tracking accuracy. In this paper we present a hybrid tracking method which can achieve real-time performance with high accuracy. Based on the same framework of a state-of-the-art visual feature tracking algorithm [5] which ensures accurate and reliable pose tracking, the proposed hybrid method significantly reduces its computational cost with the assistance of a phone's built-in inertial sensors. However, noises in inertial sensors and abrupt errors in feature tracking due to severe motion blurs could result in instability of the hybrid tracking system. To address this problem, we propose to employ an adaptive Kalman filter with abrupt error detection to robustly fuse the inertial and feature tracking results. We evaluated the proposed method on a dataset consisting of 16 video clips with synchronized inertial sensing data. Experimental results demonstrated our method's superior performance and accuracy on smartphones compared to a state-of-the-art vision tracking method [5]. The dataset will be made publicly available with the publication of this paper.

This paper aims at robust and efficient pose tracking for augmented reality on modern smartphones. Existing methods, relying on either vision analysis or motion sensing, are either too computationally expensive to achieve real-time performance on a smartphone, or too noisy to achieve sufficient robustness. This paper presents a hybrid tracking system which can achieve real-time performance with high robustness. Our system utilizes an efficient featureless method based on pixel-based registration to track the object pose on every frame. The featureless tracking result is revised from time to time by a feature-based method to reduce tracking errors. Both featureless and feature-based tracking results are sensitive to large motion blurs. To improve the robustness, an adaptive Kamlan filter is proposed to fuse the visual tracking results with the inertial tracking results computed form phone's built-in sensors. Our hybrid method is evaluated on a dataset consisting of 16 video clips with synchronized inertial sensing data. Experimental results demonstrated the superior performance of our method to state-of-the-art visual tracking methods [5, 12] on smartphones. The dataset will be made publicly available with the publication of this paper.