A Comparison of Region Detectors for Tracking
In this work, the performance of five popular region detectors is compared in the context of tracking. Firstly, conventional nearest-neighbor matching based on the similarity of region descriptors is used to assemble trajectories from unique region-to-region correspondences. Based on carefully estimated homographies between planar object surfaces in neighboring frames of an image sequence, both their localization accuracy and length, as well as the percentage of successfully tracked regions is evaluated and compared. The evaluation results serve as a supplement to existing studies and facilitate the selection of appropriate detectors suited to the requirements of a specific application. Secondly, a novel tracking method is presented, which integrates for each region all potential matches into directed multi-edge graphs. From these, trajectories are extracted using Dijkstra’s algorithm. It is shown, that the resulting localization error is significantly lower than with nearest-neighbor matching while at the same time, the percentage of tracked regions is increased.
Keywordsaffine-covariant region detection region tracking graph traversal performance evaluation
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