Feature tracking is a useful technique for studying the evolution of phenomena (or features) in time-varying scientific datasets. Time-varying datasets can be massive and are constantly becoming larger as more powerful machines are being used for scientific computations. To interactively explore such datasets feature tracking must be done efficiently. For massive datasets, which do not fit into memory, tracking should be done out-of-core. In this paper, we propose an "output-sensitive" feature tracking, which uses the pre-computed metadata to (1) enable out-of-core processing
structured datasets, (2) expedite the feature tracking processing, and (3) make the feature tracking less threshold sensitive. With the assistance of the pre-computed metadata, the complexity of the feature extraction is improved from <i>O(mlgm)</i> to <i>O(n)</i>, where <i>m</i> is the number of cells in a timestep and <i>n</i> is the number of cells in just the extracted features. Furthermore, the feature tracking's complexity is improved from <i>O(nlgn)</i> to <i>O(nlgk)</i>, where <i>k</i> is the number of cells in a feature group. The metadata computation and feature tracking can easily be adapted to the out-of-core paradigm. The effectiveness and efficiency of this algorithm is demonstrated using experiments.