Temporally enhanced image object proposals for videos

Despite the recent success of image object proposals (IOPs) for image applications, the per-frame IOPs are also important for video applications. However, the existing IOPs are extracted from each frame separately and may exhibit inconsistencies across the frames. In this paper, we propose to improve the existing IOPs by enforcing the temporal consistency through a video sequence in an on-line manner. To achieve this, we propose a novel spatio-temporal objectness measure considering both the frame level objectness as well as the temporal consistency across frames. An on-line dynamic programing technique is proposed to efficiently compute such spatio-temporal objectness. In addition, compared with the spatio-temporal video object proposals(VOPs), the proposed method supports on-line applications and provides more accurate per-frame localizations. Experiments on benchmark datasets validate its superior performance compared with the existing IOPs and VOPs.