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Object Tracking

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하위 태스크: 10
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보존된 평가 기록: 43

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.

<span style="color:grey; opacity: 0.6">( Image credit: Towards-Realtime-MOT )</span>

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Object Tracking

논문

MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Results

YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association

HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking

Robustifying 3D Perception through Least-Squares Multi-Agent Graphs Object Tracking

UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions

Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking

Visual and Memory Dual Adapter for Multi-Modal Object Tracking

R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning

USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways

Lightweight RGB-T Tracking with Mobile Vision Transformers

RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking

Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation

Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art

SAM2Auto: Auto Annotation Using FLASH

VideoMolmo: Spatio-Temporal Grounding Meets Pointing

Fully Spiking Neural Networks for Unified Frame-Event Object Tracking

SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation

Progressive Scaling Visual Object Tracking

ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking

EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking

FusionTrack: End-to-End Multi-Object Tracking in Arbitrary Multi-View Environment

Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks

Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking

ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation

Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection

Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking

LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking

Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach

Non-planar Object Detection and Identification by Features Matching and Triangulation Growth

DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking

GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity

Using Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking

Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking

SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction

Asynchronous Multi-Object Tracking with an Event Camera

CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking