ARLOT uses a least-squares graph over multi-agent bounding boxes to denoise adversarial detections and a two-stage Kalman tracking association to improve 3D multi-object tracking under point-cloud attacks.
Fooling detection alone is not enough: Adversarial attack against multiple object tracking,
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Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking
ARLOT uses a least-squares graph over multi-agent bounding boxes to denoise adversarial detections and a two-stage Kalman tracking association to improve 3D multi-object tracking under point-cloud attacks.