Multi-Object Tracking
204 papers with code • 19 benchmarks • 36 datasets
Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.
Benchmarks
These leaderboards are used to track progress in Multi-Object Tracking
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Libraries
Use these libraries to find Multi-Object Tracking models and implementations
KITTI
BDD100K
MOT17
MOTChallenge
MOT16
Virtual KITTI
UAVDT
MOT15
DanceTrack
Wildtrack
Subtasks
Latest papers with no code
Inverse Neural Rendering for Explainable Multi-Object Tracking
We propose to recast 3D multi-object tracking from RGB cameras as an \emph{Inverse Rendering (IR)} problem, by optimizing via a differentiable rendering pipeline over the latent space of pre-trained 3D object representations and retrieve the latents that best represent object instances in a given input image.
MLS-Track: Multilevel Semantic Interaction in RMOT
The new trend in multi-object tracking task is to track objects of interest using natural language.
Bayesian Nonparametrics: An Alternative to Deep Learning
Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets.
CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
Accurate detection and tracking of surrounding objects is essential to enable self-driving vehicles.
NetTrack: Tracking Highly Dynamic Objects with a Net
Most methods that solely depend on coarse-grained object cues, such as boxes and the overall appearance of the object, are susceptible to degradation due to distorted internal relationships of dynamic objects.
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
In this paper, we address the challenge of multi-object tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT.
Exploring Learning-based Motion Models in Multi-Object Tracking
In the field of multi-object tracking (MOT), traditional methods often rely on the Kalman Filter for motion prediction, leveraging its strengths in linear motion scenarios.
Learning Data Association for Multi-Object Tracking using Only Coordinates
We propose a novel Transformer-based module to address the data association problem for multi-object tracking.
Beyond MOT: Semantic Multi-Object Tracking
Current multi-object tracking (MOT) aims to predict trajectories of targets (i. e.,"where") in videos.
Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving
This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data.
