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- GitHub - ykotseruba JAAD: Annotation data for JAAD (Joint . . .
JAAD annotations are organized according to video clip names There are three types of labels, pedestrians (samples with behavior annnotations), peds (bystanders that are far away and do not interact with the driver) and people (groups of pedestrians)
- Enhancing JAAD with Knowledge Graphs for Improved Pedestrian . . .
A workflow of how the proposed knowledge graph can be used and integrated with plateforms like JAAD for further video annotation and improving the accuracy of pedestrian crossing classification algorithms
- Knowledge graph proposed extensions for JAAD for better . . .
As shown in Fig 1, features can be categorized into 3 main different classes Those centered on the pedestrian’s environment (Fig 1a), those related to other pedestrians in the street (Fig 1c), and those concerning the observed pedestrian
- Diving Deeper Into Pedestrian Behavior Understanding . . .
JAAD and PIE provide multi-modal data consisting of monocular video footage filmed from inside the moving vehicle and annotations: spatial (bounding boxes for pedes-trians and relevant objects, pedestrian poses), textual (labels describing properties of the scene, pedestrian behaviors and
- JAAD Dataset - Papers With Code
Behavior annotations specify behaviors for pedestrians that interact with or require attention of the driver For each video there are several tags (weather, locations, etc ) and timestamped behavior labels from a fixed list (e g stopped, walking, looking, etc )
- CAPformer: Pedestrian Crossing Action Prediction Using . . .
Two variants of the annotations are used in the benchmark: JAAD beh and JAAD all JAAD beh includes only pedestrians with behavioral annotations: 495 crossing and 191 non-crossing, giving rise to 374 non-crossing and 1760 crossing samples
- Semantic Segmentation of Pedestrian Groups Based on . . .
In this research, we propose to perform semantic segmentation by utilizing directional-oriented density features Density features are calculated by utilizing each joint relationship, while pedestrian direction can be predicted by calculated dot product based on shoulder, neck and hip joint
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