Summary of Mamba-fetrack: Frame-event Tracking Via State Space Model, by Ju Huang et al.
Mamba-FETrack: Frame-Event Tracking via State Space Model
by Ju Huang, Shiao Wang, Shuai Wang, Zhe Wu, Xiao Wang, Bo Jiang
First submitted to arxiv on: 28 Apr 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 In this paper, researchers propose a novel RGB-Event tracking framework called Mamba-FETrack that leverages the State Space Model (SSM) to achieve high-performance tracking while reducing computational costs. The proposed tracker uses two modality-specific Mamba backbone networks to extract features from RGB frames and Event streams, which are then fused using an interactive learning mechanism. This approach outperforms existing trackers on FELT and FE108 datasets, achieving 43.5/55.6 on the SR/PR metric while reducing memory consumption and computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to track a moving object in a video by combining information from different cameras and sensors. The researchers propose a new way to do this using a special kind of neural network called Mamba-FETrack. This method is better than previous approaches because it’s more efficient and accurate. It works by using two separate networks to analyze the video frames and sensor data, then combining them in a smart way to track the object. The result is a tracker that can accurately follow objects while using fewer computer resources. |
Keywords
» Artificial intelligence » Neural network » Tracking
