Summary of Hybrid Attention For Robust Rgb-t Pedestrian Detection in Real-world Conditions, by Arunkumar Rathinam et al.
Hybrid Attention for Robust RGB-T Pedestrian Detection in Real-World Conditions
by Arunkumar Rathinam, Leo Pauly, Abd El Rahman Shabayek, Wassim Rharbaoui, Anis Kacem, Vincent Gaudillière, Djamila Aouada
First submitted to arxiv on: 6 Nov 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 The paper proposes a novel approach to improve multispectral pedestrian detection under challenging illumination conditions, specifically addressing partial overlap and sensor failure issues. The authors introduce the Hybrid Attention (HA) mechanism to mitigate performance degradation caused by these limitations. They also develop an improved RGB-T fusion algorithm that is robust against partial overlap and sensor failure. To cope with resource constraints in embedded systems, a mobile-friendly backbone is leveraged. Experimental results demonstrate the superiority of their approach over state-of-the-art methods in handling real-world challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to make self-driving cars better at detecting people on the road. They’re working on a special type of camera that uses heat and light to take pictures. This helps them detect people even when it’s dark or sunny. The problem is that sometimes these cameras might not get a complete picture because they don’t overlap perfectly, which can cause problems. To fix this, the researchers created a new way to combine the heat and light images called Hybrid Attention. They also made an algorithm that can handle when one of the sensors fails, which is important for real-world applications. |
Keywords
» Artificial intelligence » Attention
