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 551–600 of 1966 papers

TitleStatusHype
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering—0
Cognitive Disentanglement for Referring Multi-Object Tracking—0
3D Extended Object Tracking based on Extruded B-Spline Side View Profiles—0
Target-aware Bidirectional Fusion Transformer for Aerial Object Tracking—0
How good are deep learning methods for automated road safety analysis using video data? An experimental study—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
A Modular Pipeline for 3D Object Tracking Using RGB CamerasCode0
ReynoldsFlow: Exquisite Flow Estimation via Reynolds Transport TheoremCode0
AI-Driven Relocation Tracking in Dynamic Kitchen EnvironmentsCode0
MITracker: Multi-View Integration for Visual Object Tracking—0
Spectral-Enhanced Transformers: Leveraging Large-Scale Pretrained Models for Hyperspectral Object Tracking—0
UASTrack: A Unified Adaptive Selection Framework with Modality-Customization in Single Object TrackingCode0
Easy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking—0
CRTrack: Low-Light Semi-Supervised Multi-object Tracking Based on Consistency RegularizationCode0
V-HOP: Visuo-Haptic 6D Object Pose Tracking—0
Benchmarking Online Object Trackers for Underwater Robot Position Locking Applications—0
MEX: Memory-efficient Approach to Referring Multi-Object Tracking—0
IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model FilterCode0
Adaptive Perception for Unified Visual Multi-modal Object Tracking—0
OneTrack-M: A multitask approach to transformer-based MOT models—0
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy—0
Rethinking Vision Transformer for Object Centric Foundation ModelsCode0
Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction—0
Understanding Long Videos via LLM-Powered Entity Relation Graphs—0
Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection—0
CSAOT: Cooperative Multi-Agent System for Active Object Tracking—0
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling—0
Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking—0
SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video EditingCode0
Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions—0
An Empirical Study of Autoregressive Pre-training from Videos—0
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs—0
Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media—0
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking—0
Sensitivity of Room Impulse Responses in Changing Acoustic Environment—0
DreamTrack: Dreaming the Future for Multimodal Visual Object Tracking—0
Mono3DVLT: Monocular-Video-Based 3D Visual Language TrackingCode0
Autoregressive Sequential Pretraining for Visual Tracking—0
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes—0
Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection—0
Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency—0
GRAE-3DMOT: Geometry Relation-Aware Encoder for Online 3D Multi-Object Tracking—0
Scaling 4D Representations—0
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices—0
Tell Me What to Track: Infusing Robust Language Guidance for Enhanced Referring Multi-Object Tracking—0
Patch-level Sounding Object Tracking for Audio-Visual Question Answering—0
Visual Object Tracking across Diverse Data Modalities: A Review—0
Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach—0
Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking—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