Scale-Aware RPN for Vehicle Detection View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-10

AUTHORS

Lu Ding , Yong Wang , Robert Laganière , Xinbin Luo , Shan Fu

ABSTRACT

In this paper, we develop a scale-aware Region Proposal Network (RPN) model to address the problem of vehicle detection in challenging situations. Our model introduces two built in sub-networks which detect vehicles with scales from disjoint ranges. Therefore, the model is capable of training the specialized sub-networks for large-scale and small-scale vehicles in order to capture their unique characteristics. Meanwhile, high resolution of feature maps for handling small vehicle instances is obtained. The network model is followed by two XGBoost classifiers with bootstrapping strategy for mining hard negative examples. The method is evaluated on the challenging KITTI dataset and achieves comparable results against the state-of-the-art methods. More... »

PAGES

487-499

References to SciGraph publications

  • 2014. Geodesic Object Proposals in COMPUTER VISION – ECCV 2014
  • 2004-09. Efficient Graph-Based Image Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2015-12. ImageNet Large Scale Visual Recognition Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2014. Edge Boxes: Locating Object Proposals from Edges in COMPUTER VISION – ECCV 2014
  • 2016. Is Faster R-CNN Doing Well for Pedestrian Detection? in COMPUTER VISION – ECCV 2016
  • Book

    TITLE

    Advances in Visual Computing

    ISBN

    978-3-030-03800-7
    978-3-030-03801-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-03801-4_43

    DOI

    http://dx.doi.org/10.1007/978-3-030-03801-4_43

    DIMENSIONS

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