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 901–950 of 1966 papers

TitleStatusHype
Learning Correspondence for Deformable Objects—0
Attention-Aware Multi-View Pedestrian Tracking—0
Learning data association without data association: An EM approach to neural assignment prediction—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
MCBLT: Multi-Camera Multi-Object 3D Tracking in Long Videos—0
Learning Dual-Fused Modality-Aware Representations for RGBD Tracking—0
Memory Maps for Video Object Detection and Tracking on UAVs—0
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking—0
Aerial multi-object tracking by detection using deep association networks—0
Learning feed-forward one-shot learners—0
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation—0
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks—0
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking—0
Learning Irreducible Representations of Noncommutative Lie Groups—0
Learning Local Feature Descriptors for Multiple Object Tracking—0
Learning Mobile CNN Feature Extraction Toward Fast Computation of Visual Object Tracking—0
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking—0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations—0
Generalized Bayesian Filtering via Sequential Monte Carlo—0
Generalised Bayesian Filtering via Sequential Monte Carlo—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Learning Policies for Adaptive Tracking with Deep Feature Cascades—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking—0
Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers—0
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking—0
General Compression Framework for Efficient Transformer Object Tracking—0
Convolutional Regression for Visual Tracking—0
Learning Target Candidate Association to Keep Track of What Not to Track—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
Convolutional Recurrent Predictor: Implicit Representation for Multi-target Filtering and Tracking—0
MCTR: Multi Camera Tracking Transformer—0
Learning to associate detections for real-time multiple object tracking—0
Learning to Track Any Object—0
Measurement-wise Occlusion in Multi-object Tracking—0
MeMOT: Multi-Object Tracking with Memory—0
Learning to Update for Object Tracking with Recurrent Meta-learner—0
Learning to Visually Connect Actions and their Effects—0
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds—0
Convolutional Neural Networks in Orthodontics: a review—0
FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment—0
Fusion of Head and Full-Body Detectors for Multi-Object Tracking—0
Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection—0
Leveraging the Power of Data Augmentation for Transformer-based Tracking—0
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking—0
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images—0
Fully Spiking Neural Networks for Unified Frame-Event Object Tracking—0
Full Reference Objective Quality Assessment for Reconstructed Background Images—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