Vehicle detection framework for challenging lighting driving environment based on feature fusion method using adaptive neuro-fuzzy inference system

Dong Sung Pae, In Hwan Choi, Tae Koo Kang, Myo Taeg Lim

Research output: Contribution to journalArticle

2 Citations (Scopus)


This article proposes a new preceding vehicle detection framework for challenging lighting environments using a novel feature fusion technique based on an adaptive neuro-fuzzy inference system. A combination of two feature descriptors, the histogram of oriented gradients and local binary patterns, is adopted to improve the vehicle detection accuracy of the proposed framework, and the performance of the combination in image transformations is evaluated. Furthermore, we tested the detection performance of the proposed framework in three challenging driving conditions and filmed the test image sequences for each categorized environment of the experiments. The experimental results demonstrate that the proposed framework outperforms the conventional framework under specific driving environments with harsh lighting conditions.

Original languageEnglish
JournalInternational Journal of Advanced Robotic Systems
Issue number2
Publication statusPublished - 2018 Mar 1



  • Adaptive neuro-fuzzy inference system
  • Binary descriptor
  • Feature fusion
  • Vehicle detection
  • Visual object detection

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence

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