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Multi-view Multi-object Detection and Tracking

  • Chapter
Computer Vision

Part of the book series: Studies in Computational Intelligence ((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.

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Author information

Authors and Affiliations

  1. Queen Mary University of London, UK

    Murtaza Taj & Andrea Cavallaro

Authors
  1. Murtaza Taj
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  2. Andrea Cavallaro
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Editor information

Editors and Affiliations

  1. Department of Engineering, University of Cambridge, CB2 1PZ, Cambridge, UK

    Roberto Cipolla

  2. Dipartimento di Matematica ed Informatica, University of Catania, Viale A. Doria 6, I, 95125, Catania, Italy

    Sebastiano Battiato  & Giovanni Maria Farinella  & 

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Taj, M., Cavallaro, A. (2010). Multi-view Multi-object Detection and Tracking. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds) Computer Vision. Studies in Computational Intelligence, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12848-6_10

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  • DOI: https://doi.org/10.1007/978-3-642-12848-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12847-9

  • Online ISBN: 978-3-642-12848-6

  • eBook Packages: EngineeringEngineering (R0)

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