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 1351–1400 of 1966 papers

TitleStatusHype
Overlapping neural representations for the position of visible and imagined objects—0
Faster object tracking pipeline for real time tracking—0
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association—0
Motion Prediction on Self-driving Cars: A Review—0
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises LossCode0
SMOT: Single-Shot Multi Object TrackingCode0
Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors—0
Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks—0
Multi-object tracking with self-supervised associating network—0
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals—0
F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking—0
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking—0
Multiple Pedestrians and Vehicles Tracking in Aerial Imagery: A Comprehensive Study—0
Tracklets Predicting Based Adaptive Graph Tracking—0
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes—0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking—0
An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban ScenesCode0
Object Tracking Using Spatio-Temporal Future Prediction—0
Joint Scene and Object Tracking for Cost-Effective Augmented Reality Assisted Patient Positioning in Radiation Therapy—0
DOT: Dynamic Object Tracking for Visual SLAM—0
SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure—0
Ground-truth or DAER: Selective Re-query of Secondary InformationCode0
3D Object Detection and Tracking Based on Streaming Data—0
Hard Occlusions in Visual Object Tracking—0
MAT: Motion-Aware Multi-Object Tracking—0
Visual Object Tracking by Segmentation with Graph Convolutional Network—0
Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking—0
Learning Global Structure Consistency for Robust Object Tracking—0
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting—0
Graph Neural Networks for 3D Multi-Object Tracking—0
SoDA: Multi-Object Tracking with Soft Data Association—0
AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics—0
Factor Graph based 3D Multi-Object Tracking in Point Clouds—0
Radar-based Dynamic Occupancy Grid Mapping and Object Detection—0
Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking—0
Appearance-free Tripartite Matching for Multiple Object TrackingCode0
How Trustworthy are Performance Evaluations for Basic Vision Tasks?Code0
Integration of the 3D Environment for UAV Onboard Visual Object Tracking—0
Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping—0
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers—0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking—0
Efficient Adversarial Attacks for Visual Object Tracking—0
Object Tracking using Spatio-Temporal Networks for Future Prediction Location—0
Dense Scene Multiple Object Tracking with Box-Plane Matching—0
A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time SystemsCode0
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets—0
Object Tracking by Least Spatiotemporal Searches—0
Tracking the Untrackable—0
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—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