Debugging Object Tracking Results by a Recommender System with Correction Propagation

Achieving error-free object tracking is almost impossible for state-of-the-art tracking algorithms in challenging scenarios such as tracking a large amount of cells over months in microscopy image sequences. Meanwhile, manually debugging (verifying and correcting) tracking results object-by-object and frame-by-frame in thousands of frames is too tedious. In this paper, we propose a novel scheme to debug automated object tracking results with humans in the loop. Tracking data that are highly erroneous are recommended to annotators based on their debugging histories. Since an error found by an annotator may have many analogous errors in the tracking data and the error can also affect its nearby data, we propose a correction propagation scheme to propagate corrections from all human annotators to unchecked data, which efficiently reduces human efforts and accelerates the convergence to high tracking accuracy. Our proposed approach is evaluated on three challenging datasets. The quantitative evaluation and comparison validate that the recommender system with correction propagation is effective and efficient to help humans debug tracking results.