Nighttime vehicle detection based on bio-inspired image enhancement and weighted score-level feature fusion

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

71 Scopus Citations
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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number7555365
Pages (from-to)927-936
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume18
Issue number4
Publication statusPublished - Apr 2017

Abstract

This paper presents an effective nighttime vehicle detection system that combines a novel bioinspired image enhancement approach with a weighted feature fusion technique. Inspired by the retinal mechanism in natural visual processing, we develop a nighttime image enhancement method by modeling the adaptive feedback from horizontal cells and the center-surround antagonistic receptive fields of bipolar cells. Furthermore, we extract features based on the convolutional neural network, histogram of oriented gradient, and local binary pattern to train the classifiers with support vector machine. These features are fused by combining the score vectors of each feature with the learnt weights. During detection, we generate accurate regions of interest by combining vehicle taillight detection with object proposals. Experimental results demonstrate that the proposed bioinspired image enhancement method contributes well to vehicle detection. Our vehicle detection method demonstrates a 95.95% detection rate at 0.0575 false positives per image and outperforms some state-of-the-art techniques. Our proposed method can deal with various scenes including vehicles of different types and sizes and those with occlusions and in blurred zones. It can also detect vehicles at various locations and multiple vehicles.

Research Area(s)

  • feature extraction, high-level fusion, image enhancement, Object detection, ROI extraction

Citation Format(s)

Nighttime vehicle detection based on bio-inspired image enhancement and weighted score-level feature fusion. / Kuang, Hulin; Zhang, Xianshi; Li, Yong-Jie et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 4, 7555365, 04.2017, p. 927-936.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review