A PCA-Based Technique to Detect Moving Objects

  • Nicolas Verbeke
  • Nicole Vincent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


Moving objects detection is a crucial step for video surveillance systems. The segmentation performed by motion detection algorithms is often noisy, which makes it hard to distinguish between relevant motion and noise motion. This article describes a new approach to make such a distinction using principal component analysis (PCA), a technique not commonly used in this domain. We consider a ten-frame subsequence, where each frame is associated with one dimension of the feature space, and we apply PCA to map data in a lower-dimensional space where points picturing coherent motion are close to each other. Frames are then split into blocks that we project in this new space. Inertia ellipsoids of the projected blocks allow us to qualify the motion occurring within the blocks. The results obtained are encouraging since we get very few false positives and a satisfying number of connected components in comparison to other tested algorithms.


Data analysis motion detection principal component analysis video sequence analysis video surveillance 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Nicolas Verbeke
    • 1
  • Nicole Vincent
    • 1
  1. 1.Laboratoire CRIP5-SIP, Université René Descartes Paris 5, 45 rue des Saints-Pères, 75270 Paris Cedex 06France

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