Summary of Drivegazen: Event-based Driving Status Recognition Using Conventional Camera, by Xiaoyin Yang
DriveGazen: Event-Based Driving Status Recognition using Conventional Camera
by Xiaoyin Yang
First submitted to arxiv on: 16 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 paper introduces a wearable device for recognizing driving status, along with an open-source dataset and a real-time method for identifying driving status from eye observations. The method uses event frames generated from conventional intensity frames and an Attention Driving State Network (ADSN) to recognize driving status. Compared to event cameras, conventional cameras offer richer spatial information but lack temporal information, posing challenges in recognizing driving status. To address this issue, the paper utilizes video frames to generate synthetic DVS events, adopts a spiking neural network to decode temporal information, and uses ADSN to extract spatial cues and guide attention during training and inference. The approach is validated on the Driving Status (DriveGaze) dataset and the Single-eye Event-based Emotion (SEE) dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to recognize when someone is driving or not using a camera that’s worn by the driver. They also created a big collection of data with labeled examples, which can be used to train machines to do this task accurately. The method uses special techniques like “event frames” and “Attention Driving State Network” (ADSN) to analyze the video feed from the camera and figure out if someone is driving or not. |
Keywords
» Artificial intelligence » Attention » Inference » Neural network
