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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1026–1050 of 1966 papers

TitleStatusHype
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking—0
Tackling multiple object tracking with complicated motions—Re-designing the integration of motion and appearance—0
Tackling Occlusion in Siamese Tracking with Structured Dropouts—0
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes—0
TAO: A Large-Scale Benchmark for Tracking Any Object—0
Target-aware Bidirectional Fusion Transformer for Aerial Object Tracking—0
Target Identity-Aware Network Flow for Online Multiple Target Tracking—0
Taskology: Utilizing Task Relations at Scale—0
TDT: Teaching Detectors to Track without Fully Annotated Videos—0
Distilled Siamese Networks for Visual Tracking—0
TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos—0
Real-time Visual Object Tracking with Natural Language Description—0
Tell Me What to Track: Infusing Robust Language Guidance for Enhanced Referring Multi-Object Tracking—0
Temporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation—0
Temporally Robust Global Motion Compensation by Keypoint-based Congealing—0
Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers—0
TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking—0
The 2nd Anti-UAV Workshop & Challenge: Methods and Results—0
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024—0
The 3rd Anti-UAV Workshop & Challenge: Methods and Results—0
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks—0
The detection and rectification for identity-switch based on unfalsified control—0
The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking—0
The Mean of Multi-Object Trajectories—0
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research—0
Show:102550
← PrevPage 42 of 79Next →
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