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Multi-view Multi-object Detection and Tracking

  • Murtaza Taj
  • Andrea Cavallaro
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 285)

Abstract

Multi-viewtrackers combine data fromdifferent camera views to estimate the temporal evolution of objects across a monitored area. Data to be combined can be represented by object features (such as position, color and silhouette) or by object trajectories in each view. In this Chapter, we classify and survey state-of-the art multi-view tracking algorithms and discuss their applications and algorithmic limitations. Moreover, we present a multi-view track-before-detect approach that consistently detects and recognizes multiple simultaneous objects in a common view, based on motion models. This approach estimates the temporal evolution of objects from noisy data, given their motion model, without an explicit object detection stage.

Keywords

Multiple View Camera View Common View Camera Network Probability Hypothesis Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Murtaza Taj
    • 1
  • Andrea Cavallaro
    • 1
  1. 1.Queen Mary University of LondonUK

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