HyperAI超神经

Multiple Object Tracking On Sportsmot

评估指标

AssA

DetA

HOTA

IDF1

MOTA

评测结果

各个模型在此基准测试上的表现结果

						Paper Title
DeepEIoU + GTA	74.5	88.2	81.0	86.5	96.3	GTA: Global Tracklet Association for Multi-Object Tracking in Sports
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
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
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
MOTIP (Deformable DETR, with SportsMOT val)	65.4	86.5	75.2	78.2	96.1	Multiple Object Tracking as ID Prediction
MoveSORT	63.7	87.5	74.6	76.9	96.7	Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking
MixSort-OC	62.0	88.5	74.1	74.4	96.5	SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
MOTIP (Deformable DETR)	62.0	83.4	71.9	75.0	92.9	Multiple Object Tracking as ID Prediction
OC-SORT	61.5	88.5	73.7	74.0	96.5	Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
MeMOTR	59.1	83.1	70.0	71.4	91.5	MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
MambaMOT	58.6	86.7	71.3	71.1	94.9	MambaMOT: State-Space Model as Motion Predictor for 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
MixSort-Byte	54.8	78.8	65.7	74.1	96.2	SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
ByteTrack	52.3	78.5	64.1	71.4	95.9	ByteTrack: Multi-Object Tracking by Associating Every Detection Box
CenterTrack	48.0	82.1	62.7	60.0	90.8	Tracking Objects as Points
QDTrack	47.2	77.5	60.4	62.3	90.1	Quasi-Dense Similarity Learning for Multiple 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

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HyperAI超神经

Multiple Object Tracking On Sportsmot

评估指标

AssA

DetA

HOTA

IDF1

MOTA

评测结果

各个模型在此基准测试上的表现结果

						Paper Title
DeepEIoU + GTA	74.5	88.2	81.0	86.5	96.3	GTA: Global Tracklet Association for Multi-Object Tracking in Sports
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
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
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
MOTIP (Deformable DETR, with SportsMOT val)	65.4	86.5	75.2	78.2	96.1	Multiple Object Tracking as ID Prediction
MoveSORT	63.7	87.5	74.6	76.9	96.7	Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking
MixSort-OC	62.0	88.5	74.1	74.4	96.5	SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
MOTIP (Deformable DETR)	62.0	83.4	71.9	75.0	92.9	Multiple Object Tracking as ID Prediction
OC-SORT	61.5	88.5	73.7	74.0	96.5	Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
MeMOTR	59.1	83.1	70.0	71.4	91.5	MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
MambaMOT	58.6	86.7	71.3	71.1	94.9	MambaMOT: State-Space Model as Motion Predictor for 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
MixSort-Byte	54.8	78.8	65.7	74.1	96.2	SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
ByteTrack	52.3	78.5	64.1	71.4	95.9	ByteTrack: Multi-Object Tracking by Associating Every Detection Box
CenterTrack	48.0	82.1	62.7	60.0	90.8	Tracking Objects as Points
QDTrack	47.2	77.5	60.4	62.3	90.1	Quasi-Dense Similarity Learning for Multiple 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

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