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

Showing 901950 of 1966 papers

TitleStatusHype
Learning Correspondence for Deformable Objects0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey0
PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling0
Deep Reinforcement Learning for Visual Object Tracking in Videos0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation0
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking0
Learning feed-forward one-shot learners0
A Comparison of CNN and Classic Features for Image Retrieval0
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks0
Joint 3D Reconstruction of a Static Scene and Moving Objects0
Learning Irreducible Representations of Noncommutative Lie Groups0
Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking0
Learning Mobile CNN Feature Extraction Toward Fast Computation of Visual Object Tracking0
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations0
Isometric Multi-Shape Matching0
Learning of Global Objective for Network Flow in Multi-Object Tracking0
Deep Neural Networks0
Learning Policies for Adaptive Tracking with Deep Feature Cascades0
Is Multiple Object Tracking a Matter of Specialization?0
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking0
Is First Person Vision Challenging for Object Tracking?0
Deep Network Flow for Multi-Object Tracking0
Bags of Affine Subspaces for Robust Object Tracking0
Is First Person Vision Challenging for Object Tracking?0
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking0
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking0
Deep Multi-Shot Network for modelling Appearance Similarity in Multi-Person Tracking applications0
Inverse Neural Rendering for Explainable Multi-Object Tracking0
Learning to Track Any Object0
Introduction of a tree-based technique for efficient and real-time label retrieval in the object tracking system0
Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking0
Learning to Update for Object Tracking with Recurrent Meta-learner0
Learning to Visually Connect Actions and their Effects0
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking0
BackTrack: Robust template update via Backward Tracking of candidate template0
Background Subtraction with Real-time Semantic Segmentation0
A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data0
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results0
InterTrack: Interaction Transformer for 3D Multi-Object Tracking0
InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild0
Leveraging the Power of Data Augmentation for Transformer-based Tracking0
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking0
Deep-LK for Efficient Adaptive Object Tracking0
Interpretable Deep Tracking0
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HR-CEUTrack-LargeSuccess Rate65Unverified
2HR-CEUTrack-BaseSuccess Rate63.2Unverified
3CEUTrack-LargeSuccess Rate62.8Unverified
4CEUTrack-BaseSuccess Rate62Unverified
5SiamR-CNNSuccess Rate60.9Unverified
6TransTSuccess Rate60.5Unverified
7SuperDiMPSuccess Rate60.2Unverified
8TrDiMPSuccess Rate60.1Unverified
9KeepTrackSuccess Rate59.6Unverified
10AiATrackSuccess Rate59Unverified
#ModelMetricClaimedVerifiedStatus
1HR-MonTrack-BaseSuccess Rate68.5Unverified
2HR-MonTrack-TinySuccess Rate66.3Unverified
3Multi-modalSuccess Rate63.4Unverified
4PrDiMPSuccess Rate59Unverified
5DiMPSuccess Rate57.1Unverified
6MonTrackSuccess Rate54.9Unverified
7ATOMSuccess Rate46.5Unverified
8KYSSuccess Rate26.6Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA23.45Unverified
2DeepSORTHOTA21.16Unverified
3OC-SORTHOTA20.83Unverified
4ByteTrackHOTA20.66Unverified
5TrackFormerHOTA19.62Unverified
6HybridSORTHOTA16.64Unverified
7DiffMOTHOTA16.4Unverified
8Bot-SORTHOTA15.77Unverified
#ModelMetricClaimedVerifiedStatus
1DiMP50Success Rate67.33Unverified
2PrDiMP50Success Rate67Unverified
3PrDiMP18Success Rate65.9Unverified
4DiMP18Success Rate64.6Unverified
5AtomSuccess Rate63.8Unverified
#ModelMetricClaimedVerifiedStatus
1finalHumans0.14Unverified
2night_furyHumans0.05Unverified
3Yolo based methodHumans0.02Unverified
4finalHumans0Unverified
#ModelMetricClaimedVerifiedStatus
1M2-Trackmean precision83.4Unverified
2BATmean precision75.2Unverified
#ModelMetricClaimedVerifiedStatus
1UMMT3DMOTA95Unverified
2MMPTRACK3DMOTA94.8Unverified
#ModelMetricClaimedVerifiedStatus
1Siam-FCAverage IOU0.66Unverified
#ModelMetricClaimedVerifiedStatus
1RT-MDNetPrecision Plot0.63Unverified