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Bioinspired point cloud representation: 3D object tracking

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Abstract

The problem of processing point cloud sequences is considered in this work. In particular, a system that represents and tracks objects in dynamic scenes acquired using low-cost sensors such as the Kinect is presented. An efficient neural network-based approach is proposed to represent and estimate the motion of 3D objects. This system addresses multiple computer vision tasks such as object segmentation, representation, motion analysis and tracking. The use of a neural network allows the unsupervised estimation of motion and the representation of objects in the scene. This proposal avoids the problem of finding corresponding features while tracking moving objects. A set of experiments are presented that demonstrate the validity of our method to track 3D objects. Moreover, an optimization strategy is applied to achieve real-time processing rates. Favorable results are presented demonstrating the capabilities of the GNG-based algorithm for this task. Some videos of the proposed system are available on the project website (http://www.dtic.ua.es/~sorts/3d_object_tracking/).

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Notes

  1. Kinect for XBox 360: http://www.xbox.com/kinect Microsoft.

  2. http://www.dtic.ua.es/~sorts/3d_object_tracking/.

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Acknowledgments

This work was partially funded by the Spanish Government DPI2013-40534-R Grant.

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Authors and Affiliations

  1. Computer Technology Department, University of Alicante, Alicante, Spain

    Sergio Orts-Escolano, Jose Garcia-Rodriguez, Jorge Azorin, Marcelo Saval, Alberto Garcia-Garcia & Victor Villena

  2. Computer Science and Artificial Intelligence Department, University of Alicante, Alicante, Spain

    Miguel Cazorla & Vicente Morell

Authors
  1. Sergio Orts-Escolano
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  2. Jose Garcia-Rodriguez
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  3. Miguel Cazorla
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  4. Vicente Morell
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  7. Alberto Garcia-Garcia
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  8. Victor Villena
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Correspondence to Jose Garcia-Rodriguez.

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Orts-Escolano, S., Garcia-Rodriguez, J., Cazorla, M. et al. Bioinspired point cloud representation: 3D object tracking. Neural Comput & Applic 29, 663–672 (2018). https://doi.org/10.1007/s00521-016-2585-0

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