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Publication Detail
Automated detection of cars in transmission X-ray images of freight containers
  • Publication Type:
    Conference
  • Authors:
    Jaccard N, Rogers TW, Griffin LD
  • ISBN-13:
    978-1-4799-4871-0
  • Status:
    Accepted
  • Name of conference:
    2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
  • Conference place:
    South Korea, Seoul
  • Conference start date:
    26/08/2014
  • Conference finish date:
    29/08/2014
  • Language:
    English
  • Keywords:
    Freight, Cargo imaging, X-ray, Object detection, Machine learning
Abstract
We present a method for automated car detection in X- ray transmission images of freight containers. A random forest classifier was used to classify image sub-windows as “car” and “non-car” based on image features such as intensity and log-intensity, as well as local structures and symmetries as encoded by Basic Image Features (BIFs) and oriented Basic Image Features (oBIFs). The proposed ap- proach was validated using a dataset of stream of commerce X-ray images. A car detection rate of 100% was achieved while maintaining a false alarm rate of 1.23%. Further re- duction in false alarm rate, potentially at the cost of detec- tion rate, was possible by tweaking the classification con- fidence threshold. This work establishes a framework for the automated classification of X-ray transmission cargo images and their content, paving the way towards the de- velopment of tools to assist custom officers faced with an ever increasing number of images to inspect.
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