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 1051–1075 of 1966 papers

TitleStatusHype
TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs—0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation—0
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective—0
Interpretable Deep Tracking—0
Exploiting More Information in Sparse Point Cloud for 3D Single Object TrackingCode0
Transformers for Object Detection in Large Point Clouds—0
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
Variational Bayes for robust radar single object tracking—0
Visual Object Tracking in First Person Vision—0
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix—0
Observation Centric and Central Distance Recovery on Sports Player TrackingCode0
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking—0
D^3: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports VideosCode0
Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking—0
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild—0
Towards Frame Rate Agnostic Multi-Object TrackingCode0
HVC-Net: Unifying Homography, Visibility, and Confidence Learning for Planar Object Tracking—0
TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM—0
Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking—0
Multiple Object Tracking in Recent Times: A Literature Review—0
TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-WorldCode0
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion—0
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking—0
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained VideosCode0
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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