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
Latest papers with no code
Type-to-Track: Retrieve Any Object via Prompt-based Tracking
This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-Track, which allows users to track objects in videos by typing natural language descriptions.
S$^3$Track: Self-supervised Tracking with Soft Assignment Flow
With this training approach in hand, we develop an appearance-based model for learning instance-aware object features used to construct a cost matrix based on the pairwise distances between the object features.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge.
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation.
Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation
This fine-grained representation requires high feature resolution and precise semantic information.
Spatio-Temporal Point Process for Multiple Object Tracking
As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories.
Tracking Multiple Deformable Objects in Egocentric Videos
DETracker outperforms existing state-of-the-art method on the DogThruGlasses dataset and YouTube-Hand dataset.
UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement
Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association.
Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning
Unsupervised learning is a challenging task due to the lack of labels.
Joint Counting, Detection and Re-Identification for Multi-Object Tracking
The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously.
