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Multi-view object detection using epipolar constraints within cluttered x-ray security imagery

Isaac-Medina, B.K.S. and Willcocks, C.G. and Breckon, T.P. (2021) 'Multi-view object detection using epipolar constraints within cluttered x-ray security imagery.', 25th International Conference on Pattern Recognition (ICPR 2020) Milan, Italy, 10-15 January 2021.


Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object labels is proposed. In addition, detections are given a confidence probability based on its similarity with respect to the distribution of the distance to the epipolar line. This probability is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:11 October 2020
Date deposited:27 October 2020
Date of first online publication:05 May 2021
Date first made open access:08 November 2022

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