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Object proposals for detecting moving or static video objects need to addressissues such as speed, memory complexity and temporal consistency. We propose anefficient Video Object Proposal (VOP) generation method and show its efficacyin learning a better video object detector. A deep-learning based video objectdetector learned using the proposed VOP achieves state-of-the-art detectionperformance on the Youtube-Objects dataset. We further propose a clustering ofVOPs which can efficiently be used for detecting objects in video in astreaming fashion. As opposed to applying per-frame convolutional neuralnetwork (CNN) based object detection, our proposed method called Objects inVideo Enabler thRough LAbel Propagation (OVERLAP) needs to classify only asmall fraction of all candidate proposals in every video frame throughstreaming clustering of object proposals and class-label propagation. Sourcecode will be made available soon.

Detecting Temporally Consistent Objects in Videos through Object Class Label Propagation