Summary of Multimodal Crowd Counting with Pix2pix Gans, by Muhammad Asif Khan et al.
Multimodal Crowd Counting with Pix2Pix GANs
by Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes the use of generative adversarial networks (GANs) to generate thermal infrared (TIR) images from color (RGB) images, allowing for the training of crowd counting models that achieve higher accuracy. Current state-of-the-art methods rely on RGB images and struggle in densely crowded scenes with poor illumination. The proposed approach combines RGB and TIR images, leveraging multimodality to improve predictions. The authors utilize a Pix2Pix GAN network to translate RGB images to TIR images and demonstrate significant accuracy improvements on several crowd counting models and benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper tries to solve a problem with current crowd-counting systems. These systems often make mistakes when there are many people in a scene and it’s dark. To fix this, the authors use special computer algorithms called generative adversarial networks (GANs) to create fake thermal images from regular color images. They then train their crowd-counting models using both types of images. This helps them get more accurate results. The authors tested their approach on several benchmark datasets and found that it significantly improved the accuracy of their crowd-counting models. |
Keywords
» Artificial intelligence » Gan
