Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 404-408.doi: 10.11896/jsjkx.210100125

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Video Synopsis Based on Trajectory Spatial Relationship Analysis

QU Zhi-guo, TAN Xian-si, TANG Tang, ZHENG Jian-cheng, FEI Tai-yong   

  1. Air Force Early Warning Academy,Wuhan 430019,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:QU Zhi-guo,born in 1982,postgraduate,Ph.D,vice professor.His main research interests include early-warning surveillance,image processing and object recognition,etc.
  • Supported by:
    National Natural Science Foundation of China(61401504) and China Postdoctoral Science Foundation(2014M562562).

Abstract: Collision phenomenon is an unpleasant issue that needs to be addressed during trajectory rearrangement in video synopsis.It is usually constrained by some collision cost in the final energy function to be optimized.However,most synopsis methods compute the collision cost term repeatedly in the iterative optimization process,leading to serious computation redundancy.To solve that,a novel synopsis method based on spatial relationships between trajectories is proposed in this paper.It turns out whether two trajectories will collide or not can be determined beforehand by analyzing their spatial relationships.Accordingly,three kinds of relationship are defined and corresponding fast computation of collision cost are given.In this way,the redundancy in collision cost computation is decreased and thus improving the speed of traditional methods obviously.Experimental results demonstrate the effectiveness of the proposed method.

Key words: Collision phenomenon, Trajectory rearrangement, Trajectory relationship, Video processing, Video synopsis

CLC Number: 

  • TP391.4
[1]HÖFERLIN B,HÖFERLIN M,WEISKOPF D,et al.InformaTion-Based Adaptive Fast-forward for Visual Surveillance[J].Multimedia and Tools Applications,2011,55(1):127-150.
[2]EJAZ N,TARIQ T B,BAIK S W.Adaptive Key Frame Extraction for Video Summarization Using an Aggregation Mechanism[J].Journal of Visual Communications and Image Representation,2012,23(7):1031-1040.
[3]PETROVIC N,JOJIC N,HUANG T S.Adaptive Video Fast Forward[J].Multimedia Tools and Applications,2005,26(3):327-344.
[4]ZHU X,WU X,FAN J,et al.Exploring Video Content Structure for Hierarchical Summarization[J].Multimedia Systems,2004,10(2):98-115.
[5]CHEN Z Y.Key-Frame Extraction Using Nonparametric Clustering Based on Density Estimation[J].Computer Science,2007,34(4):119-120,162.
[6]BELO L D S,CAETANO C A,PATROCíNIO Z K G D,et al.Summarizing Video Sequence Using a Graph-Based Hierarchical Approach[J].Neurocomputing,2016(173):1001-1016.
[7]PRITCH Y,RAV-ACHA A,PELEG S.Nonchronological Video Synopsis and Indexing[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(11):1971-1984.
[8]HUANG C R,CHUNG P C J,YANG D K,et al.Maximum A Posteriori Probability Estimation for Online Surveillance Video Synopsis[J].IEEE Transactions on Circuits Systems and Video Technology,2014,24(8):1417-1429.
[9]NIE Y,XIAO C,SUN H,et al.Compact Video Synopsis viaGlobal Spatiotemporal Optimization[J].IEEE Transactions on Visualization and Computer Graphics,2013,19(10):1664-1676.
[10]ZHONG R,HU R,WANG Z,et al.Fast Synopsis for Moving Objects Using Compressed Video[J].IEEE Signal Processing Letters,2014,21(7):834-838.
[11]FU W,WANG J,GUI L,et al.Online Video Synopsis of Structured Motion[J].Neurocomputing,2014,135:155-162.
[12]ZHU J,FENG S,YI D,et al.High-Performance Video Condensation System[J].IEEE Transactions on Circuits Systems and Video Technology,2015,25(7):1113-1124.
[13]ZHU J,LIAO S,LI S Z.Multicamera Joint Video Synopsis[J].IEEE Transactions on Circuits Systems and Video Technology,2016,26 (6):1058-1069.
[14]LI X L,WANG Z G,LU X Q.Surveillance Video Synopsis via Scaling Down Objects[J].IEEE Transactions on image Processing,2016,25(2):740-755.
[15]HE Y,GAO C X,SANG N,et al.Graph Coloring Based Surveillance Video Synopsis[J].Neurocomputing,2017,225:64-79.
[16]HE Y,QU Z G,GAO C X,et al.Fast Online Video Synopsis Based on Potential Collision Graph[J].IEEE Signal Processing Letters,2017,24(1):22-26.
[17]TIAN H L,DING S,YU C W,et al.Research of Video Abstraction Based on Object Detection and Tracking[J].Computer Science,2016,43(11):297-299,312.
[18]ZHAO L,HUANG H.Compressed Domain Synopsis Research in AVS Surveillance Profile[J].Computer Science,2016,43(7):46-50.
[19]BARNICH O,DROOGENBROECK M V.Vibe:A UniversalBackground Subtraction Algorithm for Video Sequences[J].IEEE Trans.On Image Processing,2011,20(6):1709-1724.
[20]YANG T,LI S Z,PAN Q,et al.Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes[C]//Proceedings IEEE Conference on Computer Vision and Pattern Recognition.2005:970-975.
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