  Object proposals for detecting moving or static video objects need to address
issues such as speed, memory complexity and temporal consistency. We propose an
efficient Video Object Proposal (VOP) generation method and show its efficacy
in learning a better video object detector. A deep-learning based video object
detector learned using the proposed VOP achieves state-of-the-art detection
performance on the Youtube-Objects dataset. We further propose a clustering of
VOPs which can efficiently be used for detecting objects in video in a
streaming fashion. As opposed to applying per-frame convolutional neural
network (CNN) based object detection, our proposed method called Objects in
Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a
small fraction of all candidate proposals in every video frame through
streaming clustering of object proposals and class-label propagation. Source
code will be made available soon.
