Debugging Object Tracking Results by a Recommender System with Correction Propagation

  • Mingzhong Li
  • Zhaozheng YinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9009)


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.



This research was supported by NSF EPSCoR grant IIA-1355406 and NSF CAREER award IIS-1351049, University of Missouri Research Board, ISC and CBSE centers at Missouri University of Science and Technology.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceMissouri University of Science and TechnologyRollaUSA

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