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Article

Vehicle Detection Based on Probability Hypothesis Density Filter

Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany
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Academic Editor: Felipe Jimenez
Sensors 2016, 16(4), 510; https://doi.org/10.3390/s16040510
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)
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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MDPI and ACS Style

Zhang, F.; Knoll, A. Vehicle Detection Based on Probability Hypothesis Density Filter. Sensors 2016, 16, 510. https://doi.org/10.3390/s16040510

AMA Style

Zhang F, Knoll A. Vehicle Detection Based on Probability Hypothesis Density Filter. Sensors. 2016; 16(4):510. https://doi.org/10.3390/s16040510

Chicago/Turabian Style

Zhang, Feihu, and Alois Knoll. 2016. "Vehicle Detection Based on Probability Hypothesis Density Filter" Sensors 16, no. 4: 510. https://doi.org/10.3390/s16040510

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