Multiple People Tracking
8 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Multiple People Tracking
Libraries
Use these libraries to find Multiple People Tracking models and implementations
MMPTRACK
LTFT
PersonPath22
Crowd in a rally | Crowd Counting | Crowd Human
Most implemented papers
MOT16: A Benchmark for Multi-Object Tracking
PaddlePaddle/PaddleDetection
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Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
DanceTrack/DanceTrack
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CVPR 2023
In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
longcw/MOTDT
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Online multi-object tracking is a fundamental problem in time-critical video analysis applications.
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.
Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment
ipl-uw/aic23_track1_uwipl_etri
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Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs.
BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking
To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object.
MOT20: A benchmark for multi object tracking in crowded scenes
PaddlePaddle/PaddleDetection
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The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.
Do Different Tracking Tasks Require Different Appearance Models?
Zhongdao/UniTrack
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NeurIPS 2021
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.
