Rotation estimation from cloud tracking

Abstract

We address the problem of online relative orientation estimation from streaming video captured by a sky-facing camera on a mobile device. Namely, we rely on the detection and tracking of visual features attained from cloud structures. Our proposed method achieves robust and efficient operation by combining realtime visual odometry modules, learning based feature classification, and Kalman filtering within a robustness-driven data management framework, while achieving framerate processing on a mobile device. The relatively large 3D distance between the camera and the observed cloud features is leveraged to simplify our processing pipeline. First, as an efficiency driven optimization, we adopt a homography based motion model and focus on estimating relative rotations across adjacent keyframes. To this end, we rely on efficient feature extraction, KLT tracking, and RANSAC based model fitting. Second, to ensure the validity of our simplified motion model, we segregate detected cloud features from scene features through SVM classification. Finally, to make tracking more robust, we employ predictive Kalman filtering to enable feature persistence through temporary occlusions and manage feature spatial distribution to foster tracking robustness. Results exemplify the accuracy and robustness of the proposed approach and highlight its potential as a passive orientation sensor.