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 1451–1500 of 1966 papers

TitleStatusHype
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
The Progression of Transformers from Language to Vision to MOT: A Literature Review on Multi-Object Tracking with Transformers—0
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping—0
The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems—0
The Solution for Single Object Tracking Task of Perception Test Challenge 2024—0
The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA—0
The Solution Path Algorithm for Identity-Aware Multi-Object Tracking—0
The SpaceNet Multi-Temporal Urban Development Challenge—0
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking—0
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes—0
Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving—0
Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking—0
Towards Category Unification of 3D Single Object Tracking on Point Clouds—0
Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds—0
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking—0
Towards Efficient Training with Negative Samples in Visual Tracking—0
Towards Mobile Sensing with Event Cameras on High-agility Resource-constrained Devices: A Survey—0
Object Re-Identification from Point Clouds—0
Towards real-time and energy efficient Siamese tracking -- a hardware-software approach—0
TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking—0
TrackAgent: 6D Object Tracking via Reinforcement Learning—0
Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs—0
Track Boosting and Synthetic Data Aided Drone Detection—0
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition—0
TrackFlow: Multi-Object Tracking with Normalizing Flows—0
Tracking 6-DoF Object Motion from Events and Frames—0
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild—0
Tracking by 3D Model Estimation of Unknown Objects in Videos—0
Tracking by Associating Clips—0
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets—0
Tracking Deformable Parts via Dynamic Conditional Random Fields—0
Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping—0
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking—0
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters—0
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection—0
Tracking Multiple Deformable Objects in Egocentric Videos—0
Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques—0
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches—0
Tracking objects that change in appearance with phase synchrony—0
Tracking Objects with 3D Representation from Videos—0
Tracking Particles Ejected From Active Asteroid Bennu With Event-Based Vision—0
Tracking Players in a Badminton Court by Two Cameras—0
Tracking Road Users using Constraint Programming—0
Tracking Small Birds by Detection Candidate Region Filtering and Detection History-aware Association—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