Multiple Object Tracking
86 papers with code • 6 benchmarks • 14 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
HiEve
PathTrack
LPW
MMPTRACK
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.
Simple Online and Realtime Tracking
abewley/sort
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This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
ifzhang/FairMOT
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Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
Towards Real-Time Multi-Object Tracking
Zhongdao/Towards-Realtime-MOT
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ECCV 2020
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
ifzhang/ByteTrack
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arXiv 2021
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Tracking Objects as Points
xingyizhou/CenterTrack
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ECCV 2020
Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
MOT16: A Benchmark for Multi-Object Tracking
PaddlePaddle/PaddleDetection
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Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
noahcao/OC_SORT
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Multi-Object Tracking (MOT) has rapidly progressed with the development of object detection and re-identification.
Lucid Data Dreaming for Video Object Segmentation
Our approach is suitable for both single and multiple object segmentation.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
