Multiple Object Tracking
115 papers with code • 8 benchmarks • 16 datasets
Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.
Source: SOT for MOT
Benchmarks
These leaderboards are used to track progress in Multiple Object Tracking
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Libraries
Use these libraries to find Multiple Object Tracking models and implementationsDatasets
KITTI
Waymo Open Dataset
BDD100K
MOTChallenge
UA-DETRAC
KITTI MOTS
RADIATE
HiEve
SportsMOT
LPW
Latest papers
OVTrack: Open-Vocabulary Multiple Object Tracking
This leaves contemporary MOT methods limited to a small set of pre-defined object categories.
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association.
Streaming Video Model
yuzhms/streaming-video-model
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CVPR 2023
We believe that the concept of streaming video model and the implementation of S-ViT are solid steps towards a unified deep learning architecture for video understanding.
Learnable Graph Matching: A Practical Paradigm for Data Association
Data association is at the core of many computer vision tasks, e. g., multiple object tracking, image matching, and point cloud registration.
3D-POP - An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture
Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment.
Universal Instance Perception as Object Discovery and Retrieval
MasterBin-IIAU/UNINEXT
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CVPR 2023
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks.
Unifying Short and Long-Term Tracking with Graph Hierarchies
Tracking objects over long videos effectively means solving a spectrum of problems, from short-term association for un-occluded objects to long-term association for objects that are occluded and then reappear in the scene.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
DanceTrack/DanceTrack
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CVPR 2023
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance.
SportsTrack: An Innovative Method for Tracking Athletes in Sports Scenes
The SportsMOT dataset aims to solve multiple object tracking of athletes in different sports scenes such as basketball or soccer.
