Glyph-based video visualization on Google Map for surveillance in smart cities

被引:9
作者
Mehboob, Fozia [1 ,2 ]
Abbas, Muhammad [1 ]
Rehman, Saad [1 ]
Khan, Shoab A. [1 ]
Jiang, Richard [2 ]
Bouridane, Ahmed [2 ]
机构
[1] Natl Univ Sci & Technol, Islamabad, Pakistan
[2] Northumbria Univ, Comp & Informat Sci, Newcastle, England
基金
英国工程与自然科学研究理事会;
关键词
Glyph; Video visualization; Traffic surveillance; Smart cities; Google Map; HIGHLY PARALLEL FRAMEWORK; HEVC MOTION ESTIMATION;
D O I
10.1186/s13640-017-0175-4
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Video visualization (VV) is considered to be an essential part of multimedia visual analytics. Many challenges have arisen from the enormous video content of cameras which can be solved with the help of data analytics and hence gaining importance. However, the rapid advancement of digital technologies has resulted in an explosion of video data, which stimulates the needs for creating computer graphics and visualization from videos. Particularly, in the paradigm of smart cities, video surveillance as a widely applied technology can generate huge amount of videos from 24/7 surveillance. In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used effectively in outdoor surveillance videos and can be used for event-aware detection. This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos. The efficacy of the proposed scheme has been shown by acquiring several unprocessed surveillance videos and by testing our algorithm on them without their pertaining field conditions. Experimental results show that the proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data.
引用
收藏
页数:16
相关论文
共 46 条
  • [11] Chen C., 2008, Handbook of Data Visualization, P179, DOI [DOI 10.1007/978-3-540-33037-0_8, DOI 10.1007/978-3-540-33037-083, 10.1007/978-3-540-33037-083]
  • [12] Chen M., 2006, IEEE T VIS COMPUT GR, V12
  • [13] Collins R. T., 2000, COMBINED TRANSPORT E, P1
  • [14] Video visualization
    Daniel, G
    Chen, M
    [J]. IEEE VISUALIZATION 2003, PROCEEDINGS, 2003, : 409 - 416
  • [15] How close are we to solving the problem of automated visual surveillance? A review of real-world surveillance, scientific progress and evaluative mechanisms
    Dee, Hannah M.
    Velastin, Sergio A.
    [J]. MACHINE VISION AND APPLICATIONS, 2008, 19 (5-6) : 329 - 343
  • [16] Dollar P., 2005, Proceedings. 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) (IEEE Cat. No. 05EX1178), P65
  • [17] Techniques for automated reverse storyboarding
    Dony, RD
    Mateer, JW
    Robinson, JA
    [J]. IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 2005, 152 (04): : 425 - 436
  • [18] Glyph-Based Video Visualization for Semen Analysis
    Duffy, Brian
    Thiyagalingam, Jeyarajan
    Walton, Simon
    Smith, David J.
    Trefethen, Anne
    Kirkman-Brown, Jackson C.
    Gaffney, Eamonn A.
    Chen, Min
    [J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2015, 21 (08) : 980 - 993
  • [19] Fan Jiang, 2007, Proceedings 2007 IEEE International Conference on Image Processing, ICIP 2007, P145
  • [20] Modelling three-dimensional trajectories by using Bezier curves with application to hand motion
    Faraway, Julian J.
    Reed, Matthew P.
    Wang, Jing
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2007, 56 : 571 - 585