Summary of Crowdmac: Masked Crowd Density Completion For Robust Crowd Density Forecasting, by Ryo Fujii et al.
CrowdMAC: Masked Crowd Density Completion for Robust Crowd Density Forecasting
by Ryo Fujii, Ryo Hachiuma, Hideo Saito
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 In this paper, researchers develop a robust crowd density forecasting model that can handle incomplete data caused by miss-detection of pedestrians. The proposed framework, CrowdMAC, is trained to forecast future crowd density maps from partially masked past maps while reconstructing the missing observations. Additionally, the authors introduce Temporal-Density-aware Masking (TDM) and multi-task masking to enhance training efficiency. The model achieves state-of-the-art performance on seven large-scale datasets and demonstrates robustness against synthetic and realistic miss-detections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new approach to predict how many people will be in a certain area based on pictures taken before. It’s hard because sometimes the cameras can’t see all of the people, so this method tries to fix that problem by looking at what happened in the past and using that information to make a better guess about what will happen in the future. |
Keywords
» Artificial intelligence » Multi task
