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.
Motion planning in dynamic environments using velocity obstacles,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2019 3verdicts
UNVERDICTED 3representative citing papers
A multi-agent RL framework for unlabeled multi-robot planning that uses velocity obstacle projections to guarantee collision-free trajectories applicable to arbitrary robot models.
Simulation study demonstrating decentralized leader-follower control for collision-free traffic management among multiple rigid payload transport systems.
citing papers explorer
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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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Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
A multi-agent RL framework for unlabeled multi-robot planning that uses velocity obstacle projections to guarantee collision-free trajectories applicable to arbitrary robot models.
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Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems
Simulation study demonstrating decentralized leader-follower control for collision-free traffic management among multiple rigid payload transport systems.