SOTAVerified

Object Tracking

Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.

( Image credit: Towards-Realtime-MOT )

Papers

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Showing 901–925 of 1966 papers

TitleStatusHype
Learning Correspondence for Deformable Objects—0
A Comparison of CNN and Classic Features for Image Retrieval—0
Joint 3D Reconstruction of a Static Scene and Moving Objects—0
Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking—0
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability—0
Isometric Multi-Shape Matching—0
Deep Neural Networks—0
Is Multiple Object Tracking a Matter of Specialization?—0
Is First Person Vision Challenging for Object Tracking?—0
Learning feed-forward one-shot learners—0
Deep Network Flow for Multi-Object Tracking—0
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks—0
Bags of Affine Subspaces for Robust Object Tracking—0
Learning Irreducible Representations of Noncommutative Lie Groups—0
Is First Person Vision Challenging for Object Tracking?—0
Learning Mobile CNN Feature Extraction Toward Fast Computation of Visual Object Tracking—0
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification—0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations—0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking—0
Learning of Global Objective for Network Flow in Multi-Object Tracking—0
Deep Multi-Shot Network for modelling Appearance Similarity in Multi-Person Tracking applications—0
Learning Policies for Adaptive Tracking with Deep Feature Cascades—0
Inverse Neural Rendering for Explainable Multi-Object Tracking—0
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking—0
Introduction of a tree-based technique for efficient and real-time label retrieval in the object tracking system—0
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All datasetsCOESOTFE108QuadTrackSeaDronesSeeBIRDSAI - ICVGIP 2020KITTIMMPTRACKPerception TestVisEvent

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HR-CEUTrack-LargeSuccess Rate65—Unverified
2HR-CEUTrack-BaseSuccess Rate63.2—Unverified
3CEUTrack-LargeSuccess Rate62.8—Unverified
4CEUTrack-BaseSuccess Rate62—Unverified
5SiamR-CNNSuccess Rate60.9—Unverified
6TransTSuccess Rate60.5—Unverified
7SuperDiMPSuccess Rate60.2—Unverified
8TrDiMPSuccess Rate60.1—Unverified
9KeepTrackSuccess Rate59.6—Unverified
10AiATrackSuccess Rate59—Unverified
#ModelMetricClaimedVerifiedStatus
1HR-MonTrack-BaseSuccess Rate68.5—Unverified
2HR-MonTrack-TinySuccess Rate66.3—Unverified
3Multi-modalSuccess Rate63.4—Unverified
4PrDiMPSuccess Rate59—Unverified
5DiMPSuccess Rate57.1—Unverified
6MonTrackSuccess Rate54.9—Unverified
7ATOMSuccess Rate46.5—Unverified
8KYSSuccess Rate26.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA23.45—Unverified
2DeepSORTHOTA21.16—Unverified
3OC-SORTHOTA20.83—Unverified
4ByteTrackHOTA20.66—Unverified
5TrackFormerHOTA19.62—Unverified
6HybridSORTHOTA16.64—Unverified
7DiffMOTHOTA16.4—Unverified
8Bot-SORTHOTA15.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiMP50Success Rate67.33—Unverified
2PrDiMP50Success Rate67—Unverified
3PrDiMP18Success Rate65.9—Unverified
4DiMP18Success Rate64.6—Unverified
5AtomSuccess Rate63.8—Unverified
#ModelMetricClaimedVerifiedStatus
1finalHumans0.14—Unverified
2night_furyHumans0.05—Unverified
3Yolo based methodHumans0.02—Unverified
4finalHumans0—Unverified
#ModelMetricClaimedVerifiedStatus
1M2-Trackmean precision83.4—Unverified
2BATmean precision75.2—Unverified
#ModelMetricClaimedVerifiedStatus
1UMMT3DMOTA95—Unverified
2MMPTRACK3DMOTA94.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Siam-FCAverage IOU0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1RT-MDNetPrecision Plot0.63—Unverified