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 1201–1250 of 1966 papers

TitleStatusHype
eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking—0
Emotion Based Prediction in the Context of Optimized Trajectory Planning for Immersive Learning—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting—0
End-to-end 3D Tracking with Decoupled Queries—0
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning—0
End-to-end Active Object Tracking via Reinforcement Learning—0
End-to-end Autonomous Driving using Deep Learning: A Systematic Review—0
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box—0
End-to-End Learning Deep CRF models for Multi-Object Tracking—0
End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking—0
End-to-End Multi-Object Tracking with Global Response Map—0
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning—0
End-to-end representation learning for Correlation Filter based tracking—0
End-to-end Tracking with a Multi-query Transformer—0
Engineering an Efficient Object Tracker for Non-Linear Motion—0
Enhanced Kalman with Adaptive Appearance Motion SORT for Grounded Generic Multiple Object Tracking—0
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines—0
Enhancing Multiple Object Tracking Accuracy via Quantum Annealing—0
Enhancing Self-Supervised Fine-Grained Video Object Tracking with Dynamic Memory Prediction—0
Enhancing the Association in Multi-Object Tracking via Neighbor Graph—0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation—0
Equivariant Filters for Efficient Tracking in 3D Imaging—0
Estimating Dynamic Flow Features in Groups of Tracked Objects—0
Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion—0
Estimation of Appearance and Occupancy Information in Birds Eye View from Surround Monocular Images—0
ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking—0
Evaluating Temporal Queries Over Video Feeds—0
Evaluating the Robustness of LiDAR Point Cloud Tracking Against Adversarial Attack—0
Evaluation of Object Trackers in Distorted Surveillance Videos—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Event-based Moving Object Detection and Tracking—0
Event Data Association via Robust Model Fitting for Event-based Object Tracking—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking—0
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model—0
Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking—0
Exploiting Temporal Relations on Radar Perception for Autonomous Driving—0
Exploring Causal Relationships in Visual Object Tracking—0
Exploring Dynamic Transformer for Efficient Object Tracking—0
MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking—0
Extended Object Tracking and Classification based on Linear Splines—0
Extended Object Tracking: Introduction, Overview and Applications—0
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter—0
Extended Target Tracking and Classification Using Neural Networks—0
Extending Multi-Object Tracking systems to better exploit appearance and 3D information—0
Extension of Sinkhorn Method: Optimal Movement Estimation of Agents Moving at Constant Velocity—0
Extensions of the Sign Language Recognition and Translation Corpus RWTH-PHOENIX-Weather—0
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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