Sayed, M;
Brostow, G;
(2021)
Improved Handling of Motion Blur in Online Object Detection.
In:
Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 1706-1716).
IEEE: Nashville, TN, USA.
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Abstract
We wish to detect specific categories of objects, for on-line vision systems that will run in the real world. Object detection is already very challenging. It is even harder when the images are blurred, from the camera being in a car or a hand-held phone. Most existing efforts either focused on sharp images, with easy to label ground truth, or they have treated motion blur as one of many generic corruptions.Instead, we focus especially on the details of egomotion induced blur. We explore five classes of remedies, where each targets different potential causes for the performance gap between sharp and blurred images. For example, first deblurring an image changes its human interpretability, but at present, only partly improves object detection. The other four classes of remedies address multi-scale texture, out-of-distribution testing, label generation, and conditioning by blur-type. Surprisingly, we discover that custom label generation aimed at resolving spatial ambiguity, ahead of all others, markedly improves object detection. Also, in contrast to findings from classification, we see a noteworthy boost by conditioning our model on bespoke categories of motion blur.We validate and cross-breed the different remedies experimentally on blurred COCO images and real-world blur datasets, producing an easy and practical favorite model with superior detection rates.
| Type: | Proceedings paper |
|---|---|
| Title: | Improved Handling of Motion Blur in Online Object Detection |
| Event: | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Dates: | 20 Jun 2021 - 25 Jun 2021 |
| ISBN-13: | 9781665445092 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR46437.2021.00175 |
| Publisher version: | https://doi.org/10.1109/CVPR46437.2021.00175 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computer vision, Computational modeling, Machine vision, Object detection, Cameras, Pattern recognition, Automobiles |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10143471 |
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