Characterizing Feature Matching Performance over Long Time Periods

Many computer vision applications rely on matching features of a query image to reference data sets, but little work has explored how quickly data sets become out of date. In this paper we measure feature matching performance across 5 years of time-lapse data from 20 static cameras to empirically study how feature matching is affected by changing sunlight direction, seasons, weather, and the structural changes over time in outdoor settings. We identify several trends that may be relevant in real world applications: (1) features are much more likely to match within a few days of the reference data, (2) weather and sun-direction have a large effect on feature matching, and (3) there is a slow decay over time due to physical changes in a scene, but this decay is much smaller than effects of lighting direction and weather. These trends are consistent across standard choices for feature detection (DoG, MSER) and feature description (SIFT, SURF, and DAISY). Across all choices, analysis of the feature detection and matching pipeline highlights that performance decay is mostly due to failures in key point detection rather than feature description.