Dynamic texture as foreground and background

被引:0
|
作者
Dmitry Chetverikov
Sándor Fazekas
Michal Haindl
机构
[1] Computer and Automation Research Institute (SZTAKI),
[2] Institute of Information Theory and Automation,undefined
来源
关键词
Dynamic texture; Detection; Optical flow; Background modelling; SVD; Photometric invariants; Temporal periodicity;
D O I
暂无
中图分类号
学科分类号
摘要
Depending on application, temporal texture can be viewed as either foreground or background. We address two related problems: finding regions of dynamic texture in a video and detecting moving targets in a dynamic texture. We propose efficient and fast methods for both cases. The methods can be potentially used in real-time applications of machine vision. First, we show how the optical flow residual can be used to find dynamic texture in video. The algorithm is a practical, real-time simplification of the sophisticated and powerful but time-consuming method (Fazekas et al. in Int J Comput Vis 82:48–63, 2009). We give numerous examples of detecting and segmenting fire, smoke, water and other dynamic textures in real-world videos acquired by static and moving cameras. Then we apply the singular value decomposition (SVD) to a temporal data window in a video to detect targets in dynamic texture via the residual of the largest singular value. For a dynamic background of low-temporal periodicity, such as water, no temporal periodicity analysis is needed. For a highly periodic background such as an escalator, we show that periodicity analysis can improve detection results. Applying the method proposed in Chetverikov and Fazekas (Proceedings of British machine vision conference, vol 1, pp 167–176, 2006), we find the temporal period and use the resonant SVD to detect moving targets against a time-periodic background.
引用
收藏
页码:741 / 750
页数:9
相关论文
共 50 条
  • [1] Dynamic texture as foreground and background
    Dmitry Chetverikov
    Sandor Fazekas
    Haindl, Michal
    MACHINE VISION AND APPLICATIONS, 2011, 22 (05) : 741 - 750
  • [2] A Background Foreground Competitive Model for Background Subtraction in Dynamic Background
    Rashid, M. E.
    Thomas, Vinu
    1ST GLOBAL COLLOQUIUM ON RECENT ADVANCEMENTS AND EFFECTUAL RESEARCHES IN ENGINEERING, SCIENCE AND TECHNOLOGY - RAEREST 2016, 2016, 25 : 536 - 543
  • [3] Dynamic Background Modeling for Foreground Segmentation
    Xu, Shaoqiu
    PROCEEDINGS OF THE 8TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE, 2009, : 599 - 604
  • [4] Hierarchical Foreground Detection in Dynamic Background
    Lu, Guoliang
    Kudo, Mineichi
    Toyama, Jun
    COMPUTER ANALYSIS OF IMAGES AND PATTERNS: 14TH INTERNATIONAL CONFERENCE, CAIP 2011, PT 2, 2011, 6855 : 413 - 420
  • [5] FOREGROUND AND BACKGROUND IN DYNAMIC SPATIAL ORIENTATION
    BRANDT, T
    WIST, ER
    DICHGANS, J
    PERCEPTION & PSYCHOPHYSICS, 1975, 17 (05): : 497 - 503
  • [6] Robust Tracking Using Foreground-Background Texture Discrimination
    Hieu T. Nguyen
    Arnold W. M. Smeulders
    International Journal of Computer Vision, 2006, 69 : 277 - 293
  • [7] Research on Readability of Adaptive Foreground in Dynamic Background
    Chi, Maoping
    Zhou, Lei
    INTELLIGENT HUMAN SYSTEMS INTEGRATION 2020, 2020, 1131 : 1244 - 1249
  • [8] Robust Dynamic Background Modeling for Foreground Estimation
    Ning, Qian
    Wu, Fangfang
    Dong, Weisheng
    Wu, Jinjian
    Shi, Guangming
    Li, Xin
    2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, 2022,
  • [9] Robust Dynamic Background Modeling for Foreground Estimation
    Ning, Qian
    Wu, Fangfang
    Dong, Weisheng
    Wu, Jinjian
    Shi, Guangming
    Li, Xin
    2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2022,
  • [10] Foreground Segmentation in Video Sequences with a Dynamic Background
    Tang, Chu
    Ahmad, M. Omair
    Wang, Chunyan
    2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), 2018,