Summary of Frequency-adaptive Low-latency Object Detection Using Events and Frames, by Haitian Zhang et al.
Frequency-Adaptive Low-Latency Object Detection Using Events and Frames
by Haitian Zhang, Xiangyuan Wang, Chang Xu, Xinya Wang, Fang Xu, Huai Yu, Lei Yu, Wen Yang
First submitted to arxiv on: 5 Dec 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 proposed Frequency-Adaptive Low-Latency Object Detector (FAOD) addresses challenges in fusing event cameras with RGB images for object detection. The method aligns low-frequency RGB frames with high-frequency events through an Align Module, and uses a training strategy called Time Shift to enforce alignment between Event-RGB pairs and their original representation. This approach enables the network to use high-frequency event data as a primary reference while treating low-frequency RGB images as supplementary information. Experimental results on PKU-DAVIS-SOD and DSEC-Detection datasets demonstrate that FAOD achieves state-of-the-art performance, with a 9.8-point improvement in mean average precision (mAP) compared to SODFormer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way of combining event cameras and RGB images for object detection. It’s like trying to take a picture using two different types of cameras at the same time! The problem is that these cameras have different qualities, so they need to be matched together correctly. The researchers came up with a solution called FAOD, which helps match the two camera views together. This allows the computer to use the best parts of each camera view to make better predictions about what’s in the picture. The results show that this approach is really good at detecting objects, even when the cameras are not perfectly matched. |
Keywords
» Artificial intelligence » Alignment » Mean average precision » Object detection
