Papers Object Tracking
“Object Tracking” 태그가 달린 논문 2,087편 · 필터 해제
YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association
Tracking small, agile multi-objects (SMOT), such as birds, from an Unmanned Aerial Vehicle (UAV) perspective is a highly challenging computer vision task. The difficulty stems from three main sources: the extreme scarcit…
Multi-Object TrackingObject TrackingHiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
This paper presents enhancements to the SAM2 framework for video object tracking task, addressing challenges such as occlusions, background clutter, and target reappearance. We introduce a hierarchical motion estimation …
Motion EstimationObject TrackingVideo Object TrackingRobustifying 3D Perception through Least-Squares Multi-Agent Graphs Object Tracking
The critical perception capabilities of EdgeAI systems, such as autonomous vehicles, are required to be resilient against adversarial threats, by enabling accurate identification and localization of multiple objects in t…
Autonomous VehiclesObject TrackingPLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detection
Monocular 3D object detection is crucial for scalable perception across fields like autonomous driving, robotics, and surveillance. However, progress is hindered by limited 3D annotations and the inherent ambiguity of si…
Monocular 3D Object DetectionAutonomous DrivingObject TrackingTrackingMiM: Efficient Mamba-in-Mamba Serialization for Real-time UAV Object Tracking
The Vision Transformer (ViT) model has long struggled with the challenge of quadratic complexity, a limitation that becomes especially critical in unmanned aerial vehicle (UAV) tracking systems, where data must be proces…
Computational EfficiencyObject TrackingUMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
Visual object tracking has gained promising progress in past decades. Most of the existing approaches focus on learning target representation in well-conditioned daytime data, while for the unconstrained real-world scena…
Domain AdaptationObject TrackingVisual Object TrackingMamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
Combining traditional RGB cameras with bio-inspired event cameras for robust object tracking has garnered increasing attention in recent years. However, most existing multimodal tracking algorithms depend heavily on high…
MambaObject TrackingVisual Object TrackingVisual and Memory Dual Adapter for Multi-Modal Object Tracking
Prompt-learning-based multi-modal trackers have achieved promising progress by employing lightweight visual adapters to incorporate auxiliary modality features into frozen foundation models. However, existing approaches …
Object TrackingPrompt LearningR1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Visual single object tracking aims to continuously localize and estimate the scale of a target in subsequent video frames, given only its initial state in the first frame. This task has traditionally been framed as a tem…
Object TrackingTemplate MatchingVisual Object TrackingVisual TrackingLightweight RGB-T Tracking with Mobile Vision Transformers
Single-modality object tracking (e.g., RGB-only) encounters difficulties in challenging imaging conditions, such as low illumination and adverse weather conditions. To solve this, multimodal tracking (e.g., RGB-T models)…
GPUObject TrackingRgb-T TrackingUSVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways
Object tracking in inland waterways plays a crucial role in safe and cost-effective applications, including waterborne transportation, sightseeing tours, environmental monitoring and surface rescue. Our Unmanned Surface …
Autonomous DrivingObjectObject TrackingRGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
We introduce a robust framework, RGBTrack, for real-time 6D pose estimation and tracking that operates solely on RGB data, thereby eliminating the need for depth input for such dynamic and precise object pose tracking ta…
6D Pose EstimationObjectObject TrackingPose Estimation+1Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
This paper presents a generalization of the trajectory general optimal sub-pattern assignment (GOSPA) metric for evaluating multi-object tracking algorithms that provide trajectory estimates with track-level uncertaintie…
Multi-Object TrackingObject TrackingState EstimationvalidDeep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art
Multi-object tracking (MOT) is a core task in computer vision that involves detecting objects in video frames and associating them across time. The rise of deep learning has significantly advanced MOT, particularly withi…
Deep LearningMulti-Object TrackingObject TrackingFocusing on Tracks for Online Multi-Object Tracking
Multi-object tracking (MOT) is a critical task in computer vision, requiring the accurate identification and continuous tracking of multiple objects across video frames. However, current state-of-the-art methods mainly r…
global-optimizationMulti-Object TrackingObject DetectionObject Tracking+1SAM2Auto: Auto Annotation Using FLASH
Vision-Language Models (VLMs) lag behind Large Language Models due to the scarcity of annotated datasets, as creating paired visual-textual annotations is labor-intensive and expensive. To address this bottleneck, we int…
Instance SegmentationObjectobject-detectionObject Detection+5Fully Spiking Neural Networks for Unified Frame-Event Object Tracking
The integration of image and event streams offers a promising approach for achieving robust visual object tracking in complex environments. However, current fusion methods achieve high performance at the cost of signific…
Object TrackingVisual Object TrackingSANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation
Few-shot segmentation aims to segment unseen object categories from just a handful of annotated examples. This requires mechanisms that can both identify semantically related objects across images and accurately produce …
Object TrackingSegmentationProgressive Scaling Visual Object Tracking
In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input resolution on tracking performance. Our e…
ObjectObject TrackingVisual Object TrackingReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
Referring Multi-object tracking (RMOT) is an important research field in computer vision. Its task form is to guide the models to track the objects that conform to the language instruction. However, the RMOT task commonl…
Multi-Object TrackingObjectObject TrackingReferring Multi-Object Tracking