Anomaly Detection in Crowded Scenes Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Xie:2014:CEC,
  title =        "Anomaly Detection in Crowded Scenes Using Genetic
                 Programming",
  author =       "Cheng Xie and Lin Shang",
  pages =        "1832--1839",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, Genetic programming, Real-world
                 applications",
  DOI =          "doi:10.1109/CEC.2014.6900396",
  abstract =     "Genetic programming(GP) has become an increasingly hot
                 issue in evolutionary computation due to its extensive
                 application. Anomaly detection in crowded scenes is
                 also a hot research topic in computer vision. However,
                 there are few contributions on using genetic
                 programming to detect abnormalities in crowded scenes.
                 In this paper, we focus on anomaly detection in crowded
                 scenes with genetic programming. We propose a new
                 method called Multi-Frame LBP Difference (MFLD) based
                 on Local Binary Patterns(LBP) to extract pixel-level
                 features from videos without additional complicated
                 preprocessing operations such as optical flow and
                 background subtraction. Genetic programming is employed
                 to generate an anomaly detector with the extracted
                 data. When a new video is coming, the detector can
                 classify every frame and localise the abnormality to a
                 single pixel level in real time. We validate our
                 approach on a public dataset and compare our method
                 with other traditional algorithms for video anomaly
                 detection. Experimental results indicate that our
                 method with genetic programming performs better in
                 detecting abnormalities in crowded scenes.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Cheng Xie Lin Shang

Citations