DensePeds uses Front-RVO motion prediction and Mask R-CNN sparse features to track people in crowds denser than 2 per square meter, running 4.5 times faster than prior methods and improving accuracy by 2.6% on dense datasets.
Social force model for pedestrian dynam- ics,
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AutoSpatial improves VLM spatial reasoning for social navigation by combining minimal manual supervision with auto-labeled VQA pairs and hierarchical training, showing gains up to 20.5% in action prediction over baselines.
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DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features
DensePeds uses Front-RVO motion prediction and Mask R-CNN sparse features to track people in crowds denser than 2 per square meter, running 4.5 times faster than prior methods and improving accuracy by 2.6% on dense datasets.
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AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning
AutoSpatial improves VLM spatial reasoning for social navigation by combining minimal manual supervision with auto-labeled VQA pairs and hierarchical training, showing gains up to 20.5% in action prediction over baselines.