3D Multi-Object Tracking
48 papers with code • 6 benchmarks • 6 datasets
Image: Weng et al
Benchmarks
These leaderboards are used to track progress in 3D Multi-Object Tracking
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nuScenes
Waymo Open Dataset
nuScenes LiDAR only
MOTFront
CVB
3D-ZeF
Most implemented papers
Simple Online and Realtime Tracking with a Deep Association Metric
nwojke/deep_sort
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Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
Center-based 3D Object Detection and Tracking
tianweiy/CenterPoint
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CVPR 2021
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
Joint detection and embedding (JDE) based methods usually estimate bounding boxes and embedding features of objects with a single network in Multi-Object Tracking (MOT).
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Our method estimates the object states by adopting a Kalman Filter.
EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time.
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.
Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking
Specifically, we exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density to realize the track initiation/termination and re-identification functionalities.
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.
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.
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.
