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Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
| Authors | Arian Bakhtiarnia; Qi Zhang; Alexandros Iosifidis |
| Title | Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement |
| Abstract | The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting. Such resolutions are far beyond the memory and computation limits of current GPUs, and available deep neural network architectures and training procedures are not designed for such massive inputs. Although several methods have been proposed to address these challenges, they are either limited to downsampling the input image to a small size, or borrowing from other gigapixel tasks, which are not tailored for crowd counting. In this paper, we propose a novel method called GigaZoom, which iteratively zooms into the densest areas of the image and refines coarser density maps with finer details. Through experiments, we show that GigaZoom obtains the state-of-the-art for gigapixel crowd counting and improves the accuracy of the next best method by 42%. |
| Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) |
| Date | 14-19/04/2024 |
| Location | Seoul, Korea |
| Year of Publication | 2024 |
| Url | https://doi.org/10.5281/zenodo.8086792 |
| 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.
