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A distributed approach for real-time multi-camera multiple object tracking

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Abstract

Estimating the positions of a set of moving objects captured from a network of cameras is still an open problem in Computer Vision. In this paper, a distributed and real-time approach for tracking multiple objects on multiple cameras is presented. A quantitative comparison with six state-of-the-art methods has been carried out on the publicly available PETS 2009 data set, demonstrating the effectiveness of the algorithm. Moreover, the proposed method has been tested also on a multi-camera soccer data set, showing its data fusion capabilities.

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Notes

  1. http://www.dis.uniroma1.it/~previtali.

  2. http://www.cvg.reading.ac.uk/PETS2009.

  3. http://www.milanton.de/data/.

  4. http://www.ino.it/home/spagnolo/Dataset.html.

  5. http://cs-people.bu.edu/jmzhang/tracker_hierarchy/Tracker_Hierarchy.htm.

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Authors and Affiliations

  1. Department of Computer, Control, and Management Engineering “A. Ruberti”, Sapienza University of Rome, Via Ariosto 25, 00185, Rome, Italy

    Fabio Previtali, Domenico D. Bloisi & Luca Iocchi

Authors
  1. Fabio Previtali
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  2. Domenico D. Bloisi
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  3. Luca Iocchi
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Correspondence to Fabio Previtali.

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Previtali, F., Bloisi, D.D. & Iocchi, L. A distributed approach for real-time multi-camera multiple object tracking. Machine Vision and Applications 28, 421–430 (2017). https://doi.org/10.1007/s00138-017-0827-5

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