Debugging Objects Tracking by a Recommender System with Correction Propagation in Hadoop Bigdata

In biomedical applications such as tracking hundreds of specimens over months, we assume none of the existing visual tracking approaches is capable of achieving 100 percent accuracy in these challenging real-world scenarios. Meanwhile, biological discovery and health diagnosis usually require high-quality tracking results for solid analysis. However, manually debugging (verifying and correcting) tracking results generated by automated tracking algorithms object-by-object and frame-by-frame in thousands of frames is too tedious. In this paper, we investigate how to debug automated object tracking results with humans in the loop. A novel iterative recommender system with correction propagation is proposed to assist multiple human annotators to debug tracking results in an effective, collaborative and efficient way. Tracking data that are highly erroneous are recommended to annotators based on their propagation influence and annotators' debugging histories. Different annotators debug the tracking data independently and their debugging results are collected for joint correction propagation. Since an error found by an annotator may have many analogous errors in the tracking data and the errors can also affect their 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 perfect tracking accuracy. Our proposed approach is evaluated on three challenging biological datasets. The quantitative evaluation and comparison validate that the recommender system with correction propagation is effective and efficient to help humans debug tracking results generated by automated tracking algorithms.