  Structure-from-motion (SfM) largely relies on feature tracking. In image
sequences, if disjointed tracks caused by objects moving in and out of the
field of view, occasional occlusion, or image noise, are not handled well,
corresponding SfM could be affected. This problem becomes severer for
large-scale scenes, which typically requires to capture multiple sequences to
cover the whole scene. In this paper, we propose an efficient non-consecutive
feature tracking (ENFT) framework to match interrupted tracks distributed in
different subsequences or even in different videos. Our framework consists of
steps of solving the feature `dropout' problem when indistinctive structures,
noise or large image distortion exists, and of rapidly recognizing and joining
common features located in different subsequences. In addition, we contribute
an effective segment-based coarse-to-fine SfM algorithm for robustly handling
large datasets. Experimental results on challenging video data demonstrate the
effectiveness of the proposed system.
