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 376–400 of 1966 papers

TitleStatusHype
BoostTrack: boosting the similarity measure and detection confidence for improved multiple object trackingCode3
Trashbusters: Deep Learning Approach for Litter Detection and Tracking—0
PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point CloudsCode1
SFSORT: Scene Features-based Simple Online Real-Time TrackerCode2
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking AttacksCode0
Self-Supervised Multi-Object Tracking with Path ConsistencyCode1
DepthMOT: Depth Cues Lead to a Strong Multi-Object TrackerCode1
Representation Alignment Contrastive Regularization for Multi-Object TrackingCode0
Ego-Motion Aware Target Prediction Module for Robust Multi-Object TrackingCode0
Bayesian Nonparametrics: An Alternative to Deep Learning—0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
SceneTracker: Long-term Scene Flow Estimation NetworkCode2
Enhancing Multiple Object Tracking Accuracy via Quantum Annealing—0
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes—0
Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking—0
Exploring Dynamic Transformer for Efficient Object Tracking—0
OmniVid: A Generative Framework for Universal Video UnderstandingCode2
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation—0
Spike-NeRF: Neural Radiance Field Based On Spike Camera—0
Elysium: Exploring Object-level Perception in Videos via MLLMCode2
Multiple Object Tracking as ID PredictionCode3
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object TrackingCode2
Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking—0
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture SearchCode1
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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