Vehicle detection based on probability hypothesis density filter

Feihu Zhang, Alois Knoll

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish
Article number510
JournalSensors (Switzerland)
Volume16
Issue number4
DOIs
StatePublished - 9 Apr 2016

Keywords

  • LiDAR
  • Vehicle detection

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