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Multi Object Tracking On Mot16

評価指標

IDF1
MOTA

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
IDF1
MOTA
Paper TitleRepository
QDTrack67.169.8Quasi-Dense Similarity Learning for Multiple Object Tracking
FairMOT-74.9FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
DEFT-68.03DEFT: Detection Embeddings for Tracking
OUTrack_fm71.174.2Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation-
HopTrack[Embedded GPU]-63.12HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices
MPNTrack61.758.6Learning a Neural Solver for Multiple Object Tracking
NOMT-46.4Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor-
LMOT72.373.2LMOT: Efficient Light-Weight Detection and Tracking in Crowds
FWT-47.8Fusion of Head and Full-Body Detectors for Multi-Object 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
TNT-49.2Exploit the Connectivity: Multi-Object Tracking with TrackletNet
JDE-64.4Towards Real-Time Multi-Object Tracking
DMMOT-46.1Online Multi-Object Tracking with Dual Matching Attention Networks
GCRA-48.2Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking-
SGT73.576.8Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
Lif_T64.761.3Lifted Disjoint Paths with Application in Multiple Object Tracking
PPTracking-77.7PP-YOLOE: An evolved version of YOLO
TraDeS64.770.1Track to Detect and Segment: An Online Multi-Object Tracker-
ReMOT-76.9ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation-
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