Online Multi-Object Tracking
25 papers with code • 5 benchmarks • 9 datasets
The goal of Online Multi-Object Tracking is to estimate the spatio-temporal trajectories of multiple objects in an online video stream (i.e., the video is provided frame-by-frame), which is a fundamental problem for numerous real-time applications, such as video surveillance, autonomous driving, and robot navigation.
Source: A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
Benchmarks
These leaderboards are used to track progress in Online Multi-Object Tracking
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Online Multi-Object Tracking models and implementationsDatasets
MOT17
MOTChallenge
MOT16
MOT15
MMPTRACK
BEE23
Oxford Town Center
PersonPath22
CholecTrack20
Latest papers
SFSORT: Scene Features-based Simple Online Real-Time Tracker
This paper introduces SFSORT, the world's fastest multi-object tracking system based on experiments conducted on MOT Challenge datasets.
Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
mikel-brostrom/yolo_tracking
•
•
Also, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner.
Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
hyunjs/sgt
•
•
The strong edge features allow SGT to track targets with tracking candidates selected by top-K scored detections with large K. As a result, even low-scored detections can be tracked, and the missed detections are also recovered.
PP-YOLOE: An evolved version of YOLO
PaddlePaddle/PaddleDetection
•
•
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Large-Scale Pre-training for Person Re-identification with Noisy Labels
DengpanFu/LUPerson
•
•
CVPR 2022
Since theses ID labels automatically derived from tracklets inevitably contain noises, we develop a large-scale Pre-training framework utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning.
Do Different Tracking Tasks Require Different Appearance Models?
Zhongdao/UniTrack
•
•
NeurIPS 2021
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.
SiamMOT: Siamese Multi-Object Tracking
amazon-research/siam-mot
•
•
CVPR 2021
In this paper, we focus on improving online multi-object tracking (MOT).
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking
jiaweihe1996/GMTracker
•
•
CVPR 2021
Then the association problem turns into a general graph matching between tracklet graph and detection graph.
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
alanzty/mo3tr
•
•
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.
Learning to Track with Object Permanence
TRI-ML/permatrack
•
•
ICCV 2021
In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning.
