SOTAVerified

Multi-Object Tracking

Multi-Object Tracking is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.

Papers

Showing 151200 of 671 papers

TitleStatusHype
SimpleTrack: Understanding and Rethinking 3D Multi-object TrackingCode1
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking TrackersCode1
Self-Supervised Multi-Object Tracking with Cross-Input ConsistencyCode1
PolyTrack: Tracking with Bounding PolygonsCode1
ByteTrack: Multi-Object Tracking by Associating Every Detection BoxCode1
Exploring Simple 3D Multi-Object Tracking for Autonomous DrivingCode1
Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle TrackingCode1
Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous DrivingCode1
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORTCode1
Score refinement for confidence-based 3D multi-object trackingCode1
Do Different Tracking Tasks Require Different Appearance Models?Code1
SiamMOT: Siamese Multi-Object TrackingCode1
Multi-object Tracking with Tracked Object Bounding Box AssociationCode1
MOTR: End-to-End Multiple-Object Tracking with TransformerCode1
EagerMOT: 3D Multi-Object Tracking via Sensor FusionCode1
Local Metrics for Multi-Object TrackingCode1
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object TrackingCode1
TransCenter: Transformers with Dense Representations for Multiple-Object TrackingCode1
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal TransformersCode1
Learning to Track with Object PermanenceCode1
Tracking Pedestrian Heads in Dense CrowdCode1
Track to Detect and Segment: An Online Multi-Object TrackerCode1
Model-free Vehicle Tracking and State Estimation in Point Cloud SequencesCode1
4D Panoptic LiDAR SegmentationCode1
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger SafetyCode1
DEFT: Detection Embeddings for TrackingCode1
Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object TrackingCode1
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for TrackingCode1
HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPSCode1
CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language DescriptionsCode1
Horizontal-to-Vertical Video ConversionCode1
TrackFormer: Multi-Object Tracking with TransformersCode1
Continuous Copy-Paste for One-Stage Multi-Object Tracking and SegmentationCode1
Assignment-Space-Based Multi-Object Tracking and SegmentationCode1
TransTrack: Multiple Object Tracking with TransformerCode1
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous DrivingCode1
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object DetectionCode1
Rethinking the competition between detection and ReID in Multi-Object TrackingCode1
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn NormalizationCode1
HOTA: A Higher Order Metric for Evaluating Multi-Object TrackingCode1
Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity FusionCode1
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli FilterCode1
PointTrack++ for Effective Online Multi-Object Tracking and SegmentationCode1
Segment as Points for Efficient Online Multi-Object Tracking and SegmentationCode1
Joint Object Detection and Multi-Object Tracking with Graph Neural NetworksCode1
3D-ZeF: A 3D Zebrafish Tracking Benchmark DatasetCode1
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature LearningCode1
Benchmarking Unsupervised Object Representations for Video SequencesCode1
Quasi-Dense Similarity Learning for Multiple Object TrackingCode1
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training ModelCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1STGTMOTA76.7Unverified
2TransCenterMOTA76Unverified
3TrackFormerMOTA74.1Unverified
4FairMOTMOTA73.7Unverified
5OUTrack_fmMOTA73.5Unverified
6LMOTMOTA72Unverified
7TraDeSMOTA69.1Unverified
8TADNMOTA69Unverified
9QDTrackMOTA68.7Unverified
10CenterTrack + TrajEMOTA67.8Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2MOTHOTA75.9Unverified
2MOTIP (Deformable DETR, with DanceTrack val and CrowdHuman)HOTA73.7Unverified
3MOTRv2HOTA73.4Unverified
4MOTIP (Deformable DETR, with CrowdHuman)HOTA71.4Unverified
5MOTIP (DAB-Deformable DETR)HOTA70Unverified
6CO-MOTHOTA69.4Unverified
7CAMELTrack (fully online)HOTA69.3Unverified
8MeMOTRHOTA68.5Unverified
9MOTIP (Deformable DETR)HOTA67.5Unverified
10MT_IOTHOTA66.66Unverified
#ModelMetricClaimedVerifiedStatus
1STGTMOTA77.5Unverified
2TransCenterMOTA72.4Unverified
3OUTrack_fmMOTA68.5Unverified
4GSDTMOTA67.1Unverified
5BoostTrack++HOTA66.4Unverified
6BoostTrack+HOTA66.2Unverified
7TrackTrackHOTA65.7Unverified
8AdapTrackHOTA65Unverified
9CMTrackHOTA64.8Unverified
10Deep OC-SORTHOTA63.9Unverified
#ModelMetricClaimedVerifiedStatus
1PPTrackingMOTA77.7Unverified
2ReMOTMOTA76.9Unverified
3SGTMOTA76.8Unverified
4STGTMOTA76.7Unverified
5FairMOTMOTA74.9Unverified
6UniTrackMOTA74.7Unverified
7OUTrack_fmMOTA74.2Unverified
8LMOTMOTA73.2Unverified
9TraDeSMOTA70.1Unverified
10QDTrackMOTA69.8Unverified
#ModelMetricClaimedVerifiedStatus
1DeepEIoU + GTAHOTA81Unverified
2HAT-FastReID-MOTHOTA80.8Unverified
3CAMELTrack (fully online)HOTA80.4Unverified
4Deep HM-SORTHOTA80.1Unverified
5AEDHOTA79.1Unverified
6DeepMoveSORTHOTA78.7Unverified
7Deep-EIoUHOTA77.2Unverified
8MoveSORTHOTA74.6Unverified
9ETTrackHOTA74.3Unverified
10MixSort-OCHOTA74.1Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA26.92Unverified
2OC-SORTHOTA25.04Unverified
3HybridSORTHOTA25.01Unverified
4ByteTrackHOTA25Unverified
5SORTHOTA23.49Unverified
6Bot-SORTHOTA22.9Unverified
7DeepSORTHOTA22.15Unverified
8DiffMOTHOTA19.96Unverified
9TrackFormerHOTA19.16Unverified
10MOTRv2HOTA18.22Unverified
#ModelMetricClaimedVerifiedStatus
1AED (Co-DETR)TETA55.3Unverified
2GLEE-ProTETA47.2Unverified
3GLEE-PlusTETA41.5Unverified
4GLEE-LiteTETA40.1Unverified
5AED (RegionCLIP)TETA37Unverified
6TETer-HTCTETA36.85Unverified
7TETer-SwinTTETA34.61Unverified
8TETerTETA33.25Unverified
9AOATETA25.27Unverified
#ModelMetricClaimedVerifiedStatus
1MCBLTIDF195.6Unverified
2TrackTacular (Bilinear Sampling)IDF195.3Unverified
3MVFlowIDF193.5Unverified
4EarlyBirdIDF192.3Unverified
5ReSTIDF186.7Unverified
6DMCT StackIDF181.9Unverified
7DMCTIDF177.8Unverified
8GLMB-YOLOv3IDF174.3Unverified
9GLMB-DOIDF172.5Unverified
#ModelMetricClaimedVerifiedStatus
1MCBLTHOTA81.22Unverified
2YachiyoHOTA71.94Unverified
3SJTU-LenovoHOTA67.22Unverified
4NotaHOTA60.93Unverified
5FraunhoferIOSBHOTA60.88Unverified
6UW-ETRIHOTA57.14Unverified
7ARVHOTA51.06Unverified
8AsillaHOTA40.34Unverified
#ModelMetricClaimedVerifiedStatus
1ReMOTSsMOTSA70.4Unverified
2UniTracksMOTSA68.9Unverified
3UnicornsMOTSA65.3Unverified
4TrackFormersMOTSA54.9Unverified
5TraDessMOTSA50.8Unverified
6Track R-CNNsMOTSA40.6Unverified