Multiple Object Tracking
114 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
Most implemented papers
Lucid Data Dreaming for Video Object Segmentation
Our approach is suitable for both single and multiple object segmentation.
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
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.
Quasi-Dense Similarity Learning for Multiple Object Tracking
Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.
Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking
Deep learning-based Multiple Object Tracking (MOT) currently relies on off-the-shelf detectors for tracking-by-detection. This results in deep models that are detector biased and evaluations that are detector influenced.
Tracking Pedestrian Heads in Dense Crowd
Sentient07/HeadHunter--T
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CVPR 2021
Moreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes.
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.
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT
AlexeyAB/darknet
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Sensors 2021
In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0. 50 of 98%.
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge.
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system.
