Outward-looking circular motion analysis of large image sequences

• Published in: IEEE transactions on pattern analysis and machine intelligence 2005-02, Vol.27 (2), p.271-277
• Main Authors: Guang Jiang, Yichen Wei, Long Quan, Sr, Hung-tat Tsui, Heung Yeung Shum, Sr
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Calibration; Cameras; circular motion; Cluster Analysis; Computer science; control theory; systems; concentric mosaic; Exact sciences and technology; Frames; Image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image motion analysis; Image sequence analysis; Image sequences; Index Terms- Structure from motion; Information Storage and Retrieval - methods; Large-scale systems; Maximum likelihood estimation; Motion; Motion analysis; Optimization; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Photography - methods; Rendering; Rendering (computer graphics); Reproducibility of Results; Sensitivity and Specificity; single axis motion; Subtraction Technique; Tracking; Trajectories; Video Recording - methods
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2005.34

This work presents a novel and simple method of analyzing the motion of a large image sequence captured by a calibrated outward-looking video camera moving on a circular trajectory for large-scale environment applications. Previous circular motion algorithms mainly focus on inward-looking turntable-like setups. They are not suitable for outward-looking motion where the conic trajectory of corresponding points degenerates to straight lines. The circular motion of a calibrated camera essentially has only one unknown rotation angle for each frame. The motion recovery for the entire sequence computes only one fundamental matrix of a pair of frames to extract the angular motion of the pair using Laguerre's formula and then propagates the computation of the unknown rotation angles to the other frames by tracking one point over at least three frames. Finally, a maximum-likelihood estimation is developed for the optimization of the whole sequence. Extensive experiments demonstrate the validity of the method and the feasibility of the application in image-based rendering.