3D Pedestrian Tracking
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Benchmarks
nuScenes validation set
Most implemented
Center-based 3D Object Detection and Tracking
Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
Papers
Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for such challenging scenes is performance-cri…
3D Pedestrian TrackingMultiple Object TrackingObject TrackingProbabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene. Current state-of-the-art follows the tracking-by-detection paradigm where existing tracks are associated with de…
3D Pedestrian TrackingAutonomous DrivingManagementMulti-Object Tracking+5Center-based 3D Object Detection and Tracking
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D wor…
3D Multi-Object Tracking3D Object Detection3D Object Tracking3D Pedestrian Tracking+53D Multi-Object Tracking: A Baseline and New Evaluation Metrics
3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to prac…
3D Multi-Object Tracking3D Pedestrian TrackingAutonomous DrivingMulti-Object Tracking+3