4D Generic Video Object Proposals
Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of neural 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. 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 combines motion cues, stereo data, and data-driven object instance segmentation in a probabilistic framework to compute a compact set of video-object proposals that precisely localizes object candidates and their contours in 3D space and time.