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홈
SOTA
Multi Object Tracking
Multi Object Tracking On Sportsmot
Multi Object Tracking On Sportsmot
평가 지표
AssA
DetA
HOTA
IDF1
MOTA
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
AssA
DetA
HOTA
IDF1
MOTA
Paper Title
Repository
QDTrack
47.2
77.5
60.4
62.3
90.1
Quasi-Dense Similarity Learning for Multiple Object Tracking
ETTrack
62.1
88.8
74.3
74.5
96.8
ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking
-
ByteTrack
52.3
78.5
64.1
71.4
95.9
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Deep HM-SORT
72.7
88.3
80.1
85.2
96.6
Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep Features, Harmonic Mean, and Expansion IOU
-
CenterTrack
48.0
82.1
62.7
60.0
90.8
Tracking Objects as Points
MixSort-OC
62.0
88.5
74.1
74.4
96.5
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
MixSort-Byte
54.8
78.8
65.7
74.1
96.2
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
OC-SORT
61.5
88.5
73.7
74.0
96.5
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
MeMOTR (Deformable-DETR)
57.8
82.0
68.8
69.9
90.2
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
TransTrack
57.5
82.7
68.9
71.5
92.6
TransTrack: Multiple Object Tracking with Transformer
MeMOTR
59.1
83.1
70.0
71.4
91.5
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
FairMOT
34.7
70.2
49.3
53.5
86.4
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
GTR
45.9
64.8
54.5
55.8
67.9
Global Tracking Transformers
MambaMOT
58.6
86.7
71.3
71.1
94.9
MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking
-
DeepMoveSORT
70.3
88.1
78.7
81.7
96.5
Engineering an Efficient Object Tracker for Non-Linear Motion
-
AED
70.1
89.4
79.1
81.8
97.1
Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
MoveSORT
63.7
87.5
74.6
76.9
96.7
Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking
Deep-EIoU
67.7
88.2
77.2
79.8
96.3
Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports
-
DeepEIoU + GTA
74.5
88.2
81.0
86.5
96.3
GTA: Global Tracklet Association for Multi-Object Tracking in Sports
MotionTrack
61.7
88.8
74.0
74.0
96.6
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
-
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