Multi-Object Tracking
208 papers with code • 19 benchmarks • 37 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
Most implemented papers
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
DanceTrack/DanceTrack
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CVPR 2022
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association.
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
We present our 1st place solution to the Group Dance Multiple People Tracking Challenge.
Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification
gerardmaggiolino/deep-oc-sort
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Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors.
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks
terbed/Deep-rPPG
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Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG).
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.
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
Min-cost flow has been a widely used paradigm for solving data association problems in multi-object tracking (MOT).
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.
Detection and Tracking Meet Drones Challenge
We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
This new paradigm enables the network to leverage the "context" information of the geometry of objects and allows us to model the interactions among the features of multiple objects.
