Papers Multi-Object Tracking
“Multi-Object Tracking” 태그가 달린 논문 812편 · 필터 해제
NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks
Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the simpler setting of homogeneous, dense p…
Multi-Object TrackingMulti-Task LearningAutonomous DrivingFrom Detection to Association: Learning Discriminative Object Embeddings for Multi-Object Tracking
End-to-end multi-object tracking (MOT) methods have recently achieved remarkable progress by unifying detection and association within a single framework. Despite their strong detection performance, these methods suffer …
Multi-Object TrackingContrastive LearningTransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals
Tracking cells in time-lapse videos is an essential technique for monitoring cell population dynamics at a single-cell level. Current methods for cell tracking are developed on videos with mostly single, constant signals…
Multi-Object TrackingCell DetectionPhysical ID-Transfer Attacks against Multi-Object Tracking via Adversarial Trajectory
Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms have yet been widely studied. In particular…
Multi-Object TrackingAutonomous DrivingObject DetectionDM$^3$T: Harmonizing Modalities via Diffusion for Multi-Object Tracking
Multi-object tracking (MOT) is a fundamental task in computer vision with critical applications in autonomous driving and robotics. Multimodal MOT that integrates visible light and thermal infrared information is particu…
Multi-Object TrackingAutonomous DrivingObject DetectionAerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios
Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current …
Representation LearningMulti-Object TrackingObject DetectionSelfMOTR: Revisiting MOTR with Self-Generating Detection Priors
End-to-end transformer architectures have driven significant progress in multi-object tracking by unifying detection and association into a single, heuristic-free framework. Despite these benefits, poor detection perform…
Multi-Object TrackingStableTrack: Stabilizing Multi-Object Tracking on Low-Frequency Detections
Multi-object tracking (MOT) is one of the most challenging tasks in computer vision, where it is important to correctly detect objects and associate these detections across frames. Current approaches mainly focus on trac…
Multi-Object TrackingVisual TrackingStable Multi-Drone GNSS Tracking System for Marine Robots
Stable and accurate tracking is essential for marine robotics, yet Global Navigation Satellite System (GNSS) signals vanish immediately below the sea surface. Traditional alternatives suffer from error accumulation, high…
Multi-Object TrackingA Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles
With the proliferation of low altitude unmanned aerial vehicles (UAVs), visual multi-object tracking is becoming a critical security technology, demanding significant robustness even in complex environmental conditions. …
Multi-Object TrackingTracking and Segmenting Anything in Any Modality
Tracking and segmentation play essential roles in video understanding, providing basic positional information and temporal association of objects within video sequences. Despite their shared objective, existing approache…
Representation LearningMulti-Object TrackingVision-Motion-Reference Alignment for Referring Multi-Object Tracking via Multi-Modal Large Language Models
Referring Multi-Object Tracking (RMOT) extends conventional multi-object tracking (MOT) by introducing natural language references for multi-modal fusion tracking. RMOT benchmarks only describe the object's appearance, r…
Multi-Object TrackingPlugTrack: Multi-Perceptive Motion Analysis for Adaptive Fusion in Multi-Object Tracking
Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where Kalman filters serve as the standard motion predictor due to computational efficiency but inherently fail on non-linear motion p…
Computational EfficiencyDomain GeneralizationMulti-Object TrackingHand Held Multi-Object Tracking Dataset in American Football
Multi-Object Tracking (MOT) plays a critical role in analyzing player behavior from videos, enabling performance evaluation. Current MOT methods are often evaluated using publicly available datasets. However, most of the…
Multi-Object TrackingDMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms
Accurate perception of the marine environment through robust multi-object tracking (MOT) is essential for ensuring safe vessel navigation and effective maritime surveillance. However, the complicated maritime environment…
Robust Object DetectionMulti-Object TrackingMulti-Object Tracking Retrieval with LLaVA-Video: A Training-Free Solution to MOT25-StAG Challenge
In this report, we present our solution to the MOT25-Spatiotemporal Action Grounding (MOT25-StAG) Challenge. The aim of this challenge is to accurately localize and track multiple objects that match specific and free-for…
Multi-Object TrackingVideo RetrievalOmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
To address panoramic distortion, large search space, and identity ambiguity under a 360° FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues. A DynamicSSM block …
Multi-Object TrackingGenTrack: A New Generation of Multi-Object Tracking
This paper introduces a novel multi-object tracking (MOT) method, dubbed GenTrack, whose main contributions include: first-a hybrid tracking approach employing both stochastic and deterministic manners to robustly handle…
Multi-Object TrackingGenTrack2: An Improved Hybrid Approach for Multi-Object Tracking
This paper proposes a visual multi-object tracking method that jointly employs stochastic and deterministic mechanisms to ensure identifier consistency for unknown and time-varying target numbers under nonlinear dynamics…
Multi-Object TrackingGRAP-MOT: Unsupervised Graph-based Position Weighted Person Multi-camera Multi-object Tracking in a Highly Congested Space
GRAP-MOT is a new approach for solving the person MOT problem dedicated to videos of closed areas with overlapping multi-camera views, where person occlusion frequently occurs. Our novel graph-weighted solution updates a…
Multi-Object Tracking