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In this work we present two video test data sets for the novel computervision (CV) task of out of distribution tracking (OOD tracking). Here, OODobjects are understood as objects with a semantic class outside the semanticspace of an underlying image segmentation algorithm, or an instance within thesemantic space which however looks decisively different from the instancescontained in the training data. OOD objects occurring on video sequences shouldbe detected on single frames as early as possible and tracked over their timeof appearance as long as possible. During the time of appearance, they shouldbe segmented as precisely as possible. We present the SOS data set containing20 video sequences of street scenes and more than 1000 labeled frames with upto two OOD objects. We furthermore publish the synthetic CARLA-WildLife dataset that consists of 26 video sequences containing up to four OOD objects on asingle frame. We propose metrics to measure the success of OOD tracking anddevelop a baseline algorithm that efficiently tracks the OOD objects. As anapplication that benefits from OOD tracking, we retrieve OOD sequences fromunlabeled videos of street scenes containing OOD objects.

Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects