Multiple People Tracking
8 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
Use these libraries to find Multiple People Tracking models and implementations
MMPTRACK
LTFT
PersonPath22
Crowd in a rally | Crowd Counting | Crowd Human
Latest papers with no code
Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods
Generally, crowd datasets can be collected or generated from real or synthetic sources.
A Unified Multi-view Multi-person Tracking Framework
Although there is a significant development in 3D Multi-view Multi-person Tracking (3D MM-Tracking), current 3D MM-Tracking frameworks are designed separately for footprint and pose tracking.
The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge.
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark
This dataset provides a more reliable benchmark of multi-camera, multi-object tracking systems in cluttered and crowded environments.
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods.
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Standardized benchmarks are crucial for the majority of computer vision applications.
Multiple People Tracking using Body and Joint Detections
We evaluate our framework on the MOT16/17 benchmark.
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
To this end, tracklet re-identification is performed by utilizing a novel multi-stage deep network that can jointly reason about the visual appearance and spatio-temporal properties of a pair of tracklets, thereby providing a robust measure of affinity.
A Graph Transduction Game for Multi-target Tracking
Semi-supervised learning is a popular class of techniques to learn from labeled and unlabeled data.
Multiple People Tracking by Lifted Multicut and Person Re-Identification
This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions.
