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Sensors 2016, 16(4), 510;

Vehicle Detection Based on Probability Hypothesis Density Filter

Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 7 January 2016 / Revised: 29 March 2016 / Accepted: 31 March 2016 / Published: 9 April 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
Full-Text   |   PDF [2303 KB, uploaded 9 April 2016]   |  


In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI) in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD) filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art. View Full-Text
Keywords: LiDAR; vehicle detection LiDAR; vehicle detection

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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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Zhang, F.; Knoll, A. Vehicle Detection Based on Probability Hypothesis Density Filter. Sensors 2016, 16, 510.

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