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Multi Object Tracking On Mot16

Metrics

IDF1
MOTA

Results

Performance results of various models on this benchmark

Paper Title
PPTracking-77.7PP-YOLOE: An evolved version of YOLO
ReMOT-76.9ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
SGT73.576.8Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
STGT76.876.7TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
FairMOT-74.9FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
UniTrack71.874.7Do Different Tracking Tasks Require Different Appearance Models?
OUTrack_fm71.174.2Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation
LMOT72.373.2LMOT: Efficient Light-Weight Detection and Tracking in Crowds
TraDeS64.770.1Track to Detect and Segment: An Online Multi-Object Tracker
QDTrack67.169.8Quasi-Dense Similarity Learning for Multiple Object Tracking
DEFT-68.03DEFT: Detection Embeddings for Tracking
MOTR67.066.8MOTR: End-to-End Multiple-Object Tracking with Transformer
GSDT-66.7Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
JDE-64.4Towards Real-Time Multi-Object Tracking
HopTrack[Embedded GPU]-63.12HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices
Lif_T64.761.3Lifted Disjoint Paths with Application in Multiple Object Tracking
MPNTrack61.758.6Learning a Neural Solver for Multiple Object Tracking
DeepMOT-Tracktor-54.8How To Train Your Deep Multi-Object Tracker
MOTDT-50.9Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
TNT-49.2Exploit the Connectivity: Multi-Object Tracking with TrackletNet
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Multi Object Tracking On Mot16 | SOTA | HyperAI