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
Latest papers
SoccerNet 2023 Challenges Results
lRomul/ball-action-spotting
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More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching
Wherein, a basic module performs target association based on IoU of detection boxes between successive frames to deal with morphological change of fish; an interaction module combines IoU of detection boxes and IoU of fish entity to handle occlusions; a refind module use spatio-temporal information uses spatio-temporal information to overcome the tracking failure resulting from the missed detection by the detector under complex environment.
Object-Centric Multiple Object Tracking
amazon-science/object-centric-multiple-object-tracking
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ICCV 2023
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
mikel-brostrom/yolo_tracking
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Also, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner.
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
Experimental results on DanceTrack show that MeMOTR impressively surpasses the state-of-the-art method by 7. 9% and 13. 0% on HOTA and AssA metrics, respectively.
Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots
Regarding the robust tracking of vegetable plants, to solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel method of Multiple Object Tracking and Segmentation (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants.
Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports
Additionally, relying on the Kalman filter in recent tracking algorithms falls short when object motion defies its linear assumption.
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack.
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning $1700m^2$, all while maintaining a strong focus on preserving privacy.
OVTrack: Open-Vocabulary Multiple Object Tracking
This leaves contemporary MOT methods limited to a small set of pre-defined object categories.
