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 with no code
3D Multiple Object Tracking on Autonomous Driving: A Literature Review
This paper undertakes a comprehensive examination, assessment, and synthesis of the research landscape in this domain, remaining attuned to the latest developments in 3D MOT while suggesting prospective avenues for future investigation.
Forensic Video Analytic Software
This project has resulted in three research outcomes Moving Object Based Collision Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and Tampering Detection Inter-Frame Forgery.
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions
Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur.
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes.
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow
Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects.
MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion
Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception.
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation
Then, a new cross-domain MOT adaptation from existing datasets is proposed without any pre-defined human knowledge in understanding and modeling objects.
Tracking Objects with 3D Representation from Videos
In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space.
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT.
Z-GMOT: Zero-shot Generic Multiple Object Tracking
Our contributions are benchmarked through extensive experiments conducted on the Referring GMOT dataset for GMOT task.
