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Sensors 2016, 16(8), 1182; doi:10.3390/s16081182

Fast Object Motion Estimation Based on Dynamic Stixels

Departamento de Ingeniería Informática, Universidad de La Laguna, Avda. Astrofísico Francisco Sánchez, s/n, San Cristóbal de La Laguna 38271, Spain
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Academic Editor: Felipe Jimenez
Received: 22 April 2016 / Revised: 22 July 2016 / Accepted: 22 July 2016 / Published: 28 July 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)

Abstract

The stixel world is a simplification of the world in which obstacles are represented as vertical instances, called stixels, standing on a surface assumed to be planar. In this paper, previous approaches for stixel tracking are extended using a two-level scheme. In the first level, stixels are tracked by matching them between frames using a bipartite graph in which edges represent a matching cost function. Then, stixels are clustered into sets representing objects in the environment. These objects are matched based on the number of stixels paired inside them. Furthermore, a faster, but less accurate approach is proposed in which only the second level is used. Several configurations of our method are compared to an existing state-of-the-art approach to show how our methodology outperforms it in several areas, including an improvement in the quality of the depth reconstruction. View Full-Text
Keywords: stixels; object tracking; object clustering; 3D reconstruction; autonomous vehicles stixels; object tracking; object clustering; 3D reconstruction; autonomous vehicles
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Morales, N.; Morell, A.; Toledo, J.; Acosta, L. Fast Object Motion Estimation Based on Dynamic Stixels. Sensors 2016, 16, 1182.

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