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 251–300 of 1966 papers

TitleStatusHype
Mismatched: Evaluating the Limits of Image Matching Approaches and BenchmarksCode1
Estimating Dynamic Flow Features in Groups of Tracked Objects—0
ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency ModelCode0
Camouflaged Object Tracking: A BenchmarkCode0
Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes—0
BoostTrack++: using tracklet information to detect more objects in multiple object trackingCode3
MCTR: Multi Camera Tracking Transformer—0
BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
Low-Light Object Tracking: A BenchmarkCode1
CHOTA: A Higher Order Accuracy Metric for Cell TrackingCode1
MambaEVT: Event Stream based Visual Object Tracking using State Space ModelCode1
MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model—0
GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System—0
RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking—0
Panacea+: Panoramic and Controllable Video Generation for Autonomous DrivingCode3
Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters—0
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting FrameworkCode3
VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking—0
3D Single-object Tracking in Point Clouds with High Temporal Variation—0
SiamMo: Siamese Motion-Centric 3D Object TrackingCode0
Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion ApproachCode2
A Batch Update Using Multiplicative Noise Modelling for Extended Object Tracking—0
Medical SAM 2: Segment medical images as video via Segment Anything Model 2Code4
SharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis—0
MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset—0
Progressive Domain Adaptation for Thermal Infrared Object Tracking—0
Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOTCode0
Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety AnalysisCode1
CORT: Class-Oriented Real-time Tracking for Embedded Systems—0
OCTrack: Benchmarking the Open-Corpus Multi-Object Tracking—0
Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object TrackingCode0
Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check—0
Strawberry detection and counting based on YOLOv7 pruning and information based tracking algorithm—0
MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object TrackingCode1
Lost and Found: Overcoming Detector Failures in Online Multi-Object TrackingCode1
DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and ObjectsCode1
CommRad: Context-Aware Sensing-Driven Millimeter-Wave Networks—0
Manipulating a Tetris-Inspired 3D Video Representation—0
Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite SetsCode1
Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors—0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images—0
Addressing single object tracking in satellite imagery through prompt-engineered solutions—0
P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point CloudsCode2
FeatureSORT: Essential Features for Effective Tracking—0
SSP-GNN: Learning to Track via Bilevel Optimization—0
TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object TrackingCode0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
Attention Normalization Impacts Cardinality Generalization in Slot AttentionCode0
The Solution for the ICCV 2023 Perception Test Challenge 2023 -- Task 6 -- Grounded videoQA—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