Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery

Isaac-Medina, B.K.S.; Willcocks, C.G.; Breckon, T.P.

Abstract

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.

Citation

Isaac-Medina, B., Willcocks, C., & Breckon, T. (2021, January). Multi-view Object Detection Using Epipolar Constraints within Cluttered X-ray Security Imagery. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 25th International Conference on Pattern Recognition (ICPR 2020)
• Start Date: Jan 10, 2021
• End Date: Jan 15, 2021
• Acceptance Date: Oct 11, 2020
• Online Publication Date: May 5, 2021
• Publication Date: 2021
• Deposit Date: Oct 25, 2020
• Publicly Available Date: Oct 27, 2020
• Series ISSN: 1051-4651
• DOI: https://doi.org/10.1109/icpr48806.2021.9413007
• Public URL: https://durham-repository.worktribe.com/output/1141503