Motion-Aware Rapid Video Saliency Detection

Fang Guo, Wenguan Wang, Ziyi Shen, Jianbing Shena, Ling Shao, Dacheng Tao

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

In this paper, we propose a computationally efficient and consistently accurate spatiotemporal salient object detection method to identify the most noticeable object in a video sequence. Intuitively, the underlying motion in a video is a more stable saliency indicator than the apparent color cues that often contain significant variations and complex structures. Based on this observation, we build an efficient and accurate spatiotemporal saliency detection method that uses motion information as a leverage to locate the most dynamic regions in a video sequence. We first analyze the optical flow field to obtain foreground priors, and then incorporate spatial saliency features such as appearance contrasts and compactness measures, into a multi-cue integration framework to combine various saliency cues and achieve temporal consistency. Rigorous experiments on the challenging SegTrackV1, SegTrackV2, and FBMS datasets demonstrate that our method generates comparable or superior performance to state-of-the-art methods while running almost 100× faster at only 0.08 sec/frame. Promising performance and rapid speed imply that the proposed spatiotemporal saliency method can be easily involved in various vision applications.
Original languageEnglish
Pages (from-to)4887-4898
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume30
Issue number12
Early online date19 Mar 2019
DOIs
Publication statusPublished - Dec 2020

Keywords

  • Video salient object detection
  • rapid video saliency detection
  • spatiotemporal salient object detection

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