Online Video SEEDS for Temporal Window Objectness

Superpixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algo-rithms have been extended to video in order to exploit the temporal consistency between frames. However, most meth-ods are computationally too expensive for real-time appli-cations. We introduce an online, real-time video superpixel algorithm based on the recently proposed SEEDS superpix-els. A new capability is incorporated which delivers multi-ple diverse samples (hypotheses) of superpixels in the same image or video sequence. The multiple samples are shown to provide a strong cue to efficiently measure the object-ness of image windows, and we introduce the novel concept of objectness in temporal windows. Experiments show that the video superpixels achieve comparable performance to state-of-the-art offline methods while running at 30 fps on a single 2.8 GHz i7 CPU. State-of-the-art performance on objectness is also demonstrated, yet orders of magnitude faster and extended to temporal windows in video. 1