Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
📰 ArXiv cs.AI
Researchers propose a model for cross-camera distracted driver classification using feature disentanglement and contrastive learning to improve accuracy across different conditions
Action Steps
- Apply feature disentanglement to separate driver and environment features
- Utilize contrastive learning to learn camera-invariant representations
- Train the model on a dataset with diverse camera conditions to improve generalizability
- Evaluate the model on a test set with unseen camera conditions to assess its robustness
Who Needs to Know This
Computer vision engineers and AI researchers on a team can benefit from this study to develop more robust driver distraction detection systems, which can be integrated into autonomous vehicles or driver assistance systems
Key Insight
💡 Feature disentanglement and contrastive learning can improve the robustness of distracted driver classification models across different camera conditions
Share This
🚗💻 Improve distracted driver classification with feature disentanglement and contrastive learning! 📈
Key Takeaways
Researchers propose a model for cross-camera distracted driver classification using feature disentanglement and contrastive learning to improve accuracy across different conditions
Full Article
Title: Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
Abstract:
arXiv:2411.13181v3 Announce Type: replace-cross Abstract: The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under conditions that differ from the training data. In this paper, we introduce a robust model d
Abstract:
arXiv:2411.13181v3 Announce Type: replace-cross Abstract: The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under conditions that differ from the training data. In this paper, we introduce a robust model d
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