3D Multi-Object Tracking
31 papers with code • 6 benchmarks • 7 datasets
Image: Weng et al
Benchmarks
These leaderboards are used to track progress in 3D Multi-Object Tracking
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KITTI
nuScenes
Waymo Open Dataset
nuScenes LiDAR only
MOTFront
3D-ZeF
CVB
Latest papers
3D Multi-Object Tracking Based on Uncertainty-Guided Data Association
In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage.
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects.
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
synsin0/srcn3d
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Our novel sparse feature sampling module only utilizes local 2D region of interest (RoI) features calculated by the projection of 3D query boxes for further box refinement, leading to a fully-convolutional and deployment-friendly pipeline.
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association
This association mechanism realizes tracking of an object in a 2D domain when the object is far away and only detected by the camera, and updating of the 2D trajectory with 3D information obtained when the object appears in the LiDAR field of view to achieve a smooth fusion of 2D and 3D trajectories.
Graph Neural Network for Cell Tracking in Microscopy Videos
By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for the maximal paths in the graph.
Immortal Tracker: Tracklet Never Dies
We employ a simple Kalman filter for trajectory prediction and preserve the tracklet by prediction when the target is not visible.
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm.
SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World
Finally, we use a pre-rendered sparse viewpoint model to create a joint posterior probability for the object pose.
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
qcraftai/simtrack
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ICCV 2021
3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles.
