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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 1966 papers

TitleStatusHype
Non-Separable Multi-Dimensional Network Flows for Visual Computing—0
The 3rd Anti-UAV Workshop & Challenge: Methods and Results—0
A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge ComputingCode0
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales—0
TransFlow: Transformer as Flow Learner—0
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads—0
Tracking by 3D Model Estimation of Unknown Objects in Videos—0
TopTrack: Tracking Objects By Their TopCode0
Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring—0
RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking—0
Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream—0
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection BoxCode0
SDTracker: Synthetic Data Based Multi-Object Tracking—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
SiamTHN: Siamese Target Highlight Network for Visual Tracking—0
OmniTracker: Unifying Object Tracking by Tracking-with-Detection—0
MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking—0
Rt-Track: Robust Tricks for Multi-Pedestrian Tracking—0
Real-time Multi-Object Tracking Based on Bi-directional Matching—0
Modeling Continuous Motion for 3D Point Cloud Object Tracking—0
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking—0
Object-based SLAM utilizing unambiguous pose parameters considering general symmetry types—0
MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors—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