Rapid Model-Driven Annotation and Evaluation for Object Detection in Videos

Nowadays, the annotation of ground truth and the automated localisation and validation of objects in audiovisual media plays an essential role to keep pace with the large data growth. A common approach to train such classifiers is to integrate methods from machine learning that often demand multiple thousands or millions of samples. Therefore, we propose two components. The first constraints the annotation space by predefined models and allows the creation of ground truth data while providing opportunities to annotate and interpolate objects in keyframes or in-between by granting a user-friendly frame-wise access. The graphical user-interface of the second component focuses on the rapid validation of automatically pre-classified object instances in order to alter the assignment of the class label or to remove false-positives to clean-up the result list which has been successfully applied on the task of Instance Search within the TRECVid evaluation campaign.