  Many high-level video understanding methods require input in the form of
object proposals. Currently, such proposals are predominantly generated with
the help of networks that were trained for detecting and segmenting a set of
known object classes, which limits their applicability to cases where all
objects of interest are represented in the training set. This is a restriction
for automotive scenarios, where unknown objects can frequently occur. We
propose an approach that can reliably extract spatio-temporal object proposals
for both known and unknown object categories from stereo video. Our 4D Generic
Video Tubes (4D-GVT) method leverages motion cues, stereo data, and object
instance segmentation to compute a compact set of video-object proposals that
precisely localizes object candidates and their contours in 3D space and time.
We show that given only a small amount of labeled data, our 4D-GVT proposal
generator generalizes well to real-world scenarios, in which unknown categories
appear. It outperforms other approaches that try to detect as many objects as
possible by increasing the number of classes in the training set to several
thousand.
