Detailed info
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training
| Authors | Arian Bakhtiarnia; Qi Zhang1; Alexandros Iosifidis |
| Title | Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training |
| Abstract | JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings. However, applying such lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we propose a training approach called curriculum pre-training (CPT) for crowd counting on compressed images, which alleviates the drop in accuracy resulting from lossy compression. We verify the effectiveness of our approach by extensive experiments on three crowd counting datasets, two crowd counting DNN models and various levels of compression. The proposed training method is not overly sensitive to hyper-parameters, and reduces the error, particularly for heavily compressed images, by up to 19.70%. |
| Conference | 2023 IEEE Symposium Series on Computational Intelligence (SSCI 2023) |
| Date | 05-08/12/2023 |
| Location | Mexico City, Mexico |
| Year of Publication | 2023 |
| Url | https://zenodo.org/records/8086848 |
| DOI |  |
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Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
