Summary of Pedestrian Intention Prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors, by Mustafa Sakhai et al.
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors
by Mustafa Sakhai, Szymon Mazurek, Jakub Caputa, Jan K. Argasiński, Maciej Wielgosz
First submitted to arxiv on: 1 Jun 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 Spike-based neural networks, paired with specialized cameras called Dynamic Vision Sensors, show promise for improving pedestrian detection in challenging weather conditions. By leveraging the high-resolution and low-latency capabilities of DVS, researchers can evaluate the efficiency of these SNNs compared to traditional convolutional neural networks (CNNs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In bad weather, like rain or snow, it’s hard for self-driving cars to see pedestrians. Researchers used special cameras called Dynamic Vision Sensors that are good at capturing fast movements and low-light conditions. They then tested spike-based neural networks, which are similar to the brains of animals. The goal was to compare these SNNs to traditional computer vision systems. |
