Maximum a posteriori probability estimation for online surveillance video synopsis

Chun Rong Huang, Pau Choo Julia Chung, Di Kai Yang, Hsing Cheng Chen, Guan Jie Huang

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


To reduce human efforts in browsing long surveillance videos, synopsis videos are proposed. Traditional synopsis video generation applying optimization on video tubes is very time consuming and infeasible for real-time online generation. This dilemma significantly reduces the feasibility of synopsis video generation in practical situations. To solve this problem, the synopsis video generation problem is formulated as a maximum a posteriori probability (MAP) estimation problem in this paper, where the positions and appearing frames of video objects are chronologically rearranged in real time without the need to know their complete trajectories. Moreover, a synopsis table is employed with MAP estimation to decide the temporal locations of the incoming foreground objects in the synopsis video without needing an optimization procedure. As a result, the computational complexity of the proposed video synopsis generation method can be significantly reduced. Furthermore, as it does not require prescreening the entire video, this approach can be applied on online streaming videos.

Original languageEnglish
Article number6748870
Pages (from-to)1417-1429
Number of pages13
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number8
Publication statusPublished - 2014 Aug

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Electrical and Electronic Engineering


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