Summary of Closing the Gap in Human Behavior Analysis: a Pipeline For Synthesizing Trimodal Data, by Christian Stippel et al.
Closing the Gap in Human Behavior Analysis: A Pipeline for Synthesizing Trimodal Data
by Christian Stippel, Thomas Heitzinger, Rafael Sterzinger, Martin Kampel
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces a novel generative technique for creating trimodal human-focused datasets that integrate RGB, thermal, and depth modalities. The authors aim to address the shortage of HBA-specific datasets that combine these modalities by leveraging human segmentation masks derived from RGB images, combined with thermal and depth backgrounds sourced automatically. They utilize conditional image-to-image translation to synthesize depth and thermal counterparts from existing RGB data, generating trimodal data that can be used to train models for settings with limited data, poor lighting conditions, or privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to analyze human behavior using different types of cameras. Usually, we use RGB cameras because they’re easy to access and provide a lot of information. However, these cameras have some weaknesses, like being affected by lighting conditions and raising privacy concerns. To overcome these issues, the authors suggest using other camera modalities like thermal and depth cameras. These cameras are good at showing human shapes and providing context. The problem is that there aren’t many datasets available that combine these different types of cameras. This paper solves this problem by creating a new way to generate these kinds of datasets. |
Keywords
* Artificial intelligence * Translation
