HE Yi, HAN Jun, SANG Nong, QU Zhiguo, GAO Changxin. Chronological Video Synopsis via Events Rearrangement Optimization[J]. Chinese Journal of Electronics, 2018, 27(2): 399-404. doi: 10.1049/cje.2016.11.002
Citation: HE Yi, HAN Jun, SANG Nong, QU Zhiguo, GAO Changxin. Chronological Video Synopsis via Events Rearrangement Optimization[J]. Chinese Journal of Electronics, 2018, 27(2): 399-404. doi: 10.1049/cje.2016.11.002

Chronological Video Synopsis via Events Rearrangement Optimization

doi: 10.1049/cje.2016.11.002
Funds:  This work is supported by the National Natural Science Foundation of China (No.61401504, No.61401170), and China Postdoctoral Science Foundation (No.2014M562562).
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  • Corresponding author: HAN Jun (corresponding author) received the M.S. degree and the Ph.D. degree in Air Force Radar Academy, Wuhan, China in 2007 and 2010 respectively. Now he is a researcher in Air Force Radar Academy. His current research interests include technology and application of radar and electronic countermeasures equipment. Up to now, he has published more than 20 academic papers. (Email:duj81@163.com)
  • Received Date: 2016-02-24
  • Rev Recd Date: 2016-05-14
  • Publish Date: 2018-03-10
  • Video synopsis aims at retrieving interested events and reducing human labor on browsing long surveillance video. Traditional video synopsis methods based on energy function minimization is high computational and time consuming. Besides, the unchronological tubes shifting strategy may cause chaotic temporality and uncomfortable collisions. We propose a spatiotemporal events rearrangement optimization algorithm, which formulates the events rearrangement problem as iteration judgment on trajectory correlation and events compactability to generate the output synopsis video. An events subsection modification strategy has been presented to solve the spatial incompleteness of certain single event caused by imperfect object tracking. The experimental results have shown that the output synopsis video reserves the chronological order and avoids collisions of events.
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