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
Most implemented papers
How To Train Your Deep Multi-Object Tracker
yihongXU/deepMOT
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CVPR 2020
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Online Multi-Object Tracking Framework with the GMPHD Filter and Occlusion Group Management
In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection.
Learning a Neural Solver for Multiple Object Tracking
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm.
A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos
Compared to manual blood cell counting, CycleTrack achieves 96. 58 $\pm$ 2. 43% cell counting accuracy among 8 test videos with 1000 frames each compared to 93. 45% and 77. 02% accuracy for independent CenterTrack and SORT almost without additional time expense.
TransTrack: Multiple Object Tracking with Transformer
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems.
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN).
MOTR: End-to-End Multiple-Object Tracking with Transformer
Temporal modeling of objects is a key challenge in multiple object tracking (MOT).
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT).
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.
DR.VIC: Decomposition and Reasoning for Video Individual Counting
taohan10200/drnet
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CVPR 2022
Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame.
