Object Detection via Fisheye Camera
Article No.: 112, Pages 1 - 7
Abstract
During the competition, several factors that could decrease the effectiveness of the training result was quickly identified, such as the lack of distortion of provided training data, the high similarity between multiple images, and the extreme imbalance of quantity between classes. Due to the short duration of the competition, we proposed six simple-to-conduct yet proven very effective methods: 1) data filtering by skipping neighboring images, 2) data filtering to reduce high quantity classes, 3) use other datasets to replenish total quantity and abundancy, 4) develop an algorithm to distort a plain image accurately, 5) Pretrain on selected MS-COCO. Ablation studies were conducted to find out the effect of each part of the data. And found that using a selected set of MS-COCO to pre-train could increase its effect. Our work is straightforward in terms of methods used, but the result of our evaluation shows that it is reasonably solid.
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Index Terms
- Object Detection via Fisheye Camera
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
Copyright © 2023 ACM.
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Publication History
Published: 01 January 2024
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- Research-article
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- NSTC 111-2221-E-011 -162 -MY3
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MMAsia '23
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Overall Acceptance Rate 59 of 204 submissions, 29%
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