Summary of Prediction Of Occluded Pedestrians in Road Scenes Using Human-like Reasoning: Insights From the Occluroads Dataset, by Melo Castillo Angie Nataly et al.
Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset
by Melo Castillo Angie Nataly, Martin Serrano Sergio, Salinas Carlota, Sotelo Miguel Angel
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper presents the Occlusion-Rich Road Scenes with Pedestrians (OccluRoads) dataset, a collection of road scenes with partially and fully occluded pedestrians in real and virtual environments. The dataset is meticulously labeled and enriched with contextual information to capture human perception in these scenarios. Using this dataset, the authors developed a pipeline to predict the presence of occluded pedestrians, combining Knowledge Graph (KG), Knowledge Graph Embedding (KGE), and Bayesian inference. This approach achieves a F1 score of 0.91, outperforming traditional machine learning models by up to 42%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars safer by creating a special dataset for detecting people on the road who are partly or completely hidden from view. The dataset includes many different scenes and is very detailed, with information about what’s happening in each scene. Using this data, the authors developed a system that can predict when there might be an occluded pedestrian, and it works really well – better than other methods by up to 42%. |
Keywords
» Artificial intelligence » Bayesian inference » Embedding » F1 score » Knowledge graph » Machine learning
