Papers Multi-Object Tracking
“Multi-Object Tracking” 태그가 달린 논문 812편 · 필터 해제
Breaking Smooth-Motion Assumptions: A UAV Benchmark for Multi-Object Tracking in Complex and Adverse Conditions
The rapid movements and agile maneuvers of unmanned aerial vehicles (UAVs) induce significant observational challenges for multi-object tracking (MOT). However, existing UAV-perspective MOT benchmarks often lack these co…
Multi-Object TrackingOcclusion-Aware SORT: Observing Occlusion for Robust Multi-Object Tracking
Multi-object tracking (MOT) involves analyzing object trajectories and counting the number of objects in video sequences. However, 2D MOT faces challenges due to positional cost confusion arising from partial occlusion. …
Multi-Object TrackingORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking
Multi-Object Tracking (MOT) is a fundamental task in computer vision, aiming to track targets across video frames. Existing MOT methods perform well in general visual scenes, but face significant challenges and limitatio…
Multi-Object TrackingRT-RMOT: A Dataset and Framework for RGB-Thermal Referring Multi-Object Tracking
Referring Multi-Object Tracking has attracted increasing attention due to its human-friendly interactive characteristics, yet it exhibits limitations in low-visibility conditions, such as nighttime, smoke, and other chal…
Multi-Object TrackingA Two-Stage Detection-Tracking Framework for Stable Apple Quality Inspection in Dense Conveyor-Belt Environments
Industrial fruit inspection systems must operate reliably under dense multi-object interactions and continuous motion, yet most existing works evaluate detection or classification at the image level without ensuring temp…
Multi-Object TrackingHiMAP: History-aware Map-occupancy Prediction with Fallback
Accurate motion forecasting is critical for autonomous driving, yet most predictors rely on multi-object tracking (MOT) with identity association, assuming that objects are correctly and continuously tracked. When tracki…
Multi-Object TrackingTrajectory PredictionAutonomous DrivingMotion ForecastingDRMOT: A Dataset and Framework for RGBD Referring Multi-Object Tracking
Referring Multi-Object Tracking (RMOT) aims to track specific targets based on language descriptions and is vital for interactive AI systems such as robotics and autonomous driving. However, existing RMOT models rely sol…
Multi-Object TrackingAutonomous DrivingUniTrack: Differentiable Graph Representation Learning for Multi-Object Tracking
We present UniTrack, a plug-and-play graph-theoretic loss function designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified differentia…
Graph Representation LearningMulti-Object TrackingGTATrack: Winner Solution to SoccerTrack 2025 with Deep-EIoU and Global Tracklet Association
Multi-object tracking (MOT) in sports is highly challenging due to irregular player motion, uniform appearances, and frequent occlusions. These difficulties are further exacerbated by the geometric distortion and extreme…
Multi-Object TrackingDeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos
Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially under unstabilized conditions where platform jitter and the weak appearance…
Multi-Object TrackingGenerative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework
Semantic Multi-Object Tracking (SMOT) is evolving from purely geometric localization toward comprehensive video understanding. However, existing paradigms predominantly rely on closed-set interaction tags and fragmented …
Multi-Object TrackingAR-MOT: Autoregressive Multi-object Tracking
As multi-object tracking (MOT) tasks continue to evolve toward more general and multi-modal scenarios, the rigid and task-specific architectures of existing MOT methods increasingly hinder their applicability across dive…
Multi-Object TrackingCT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking
Automated analysis of volumetric medical imaging on edge devices is severely constrained by the high memory and computational demands of 3D Convolutional Neural Networks (CNNs). This paper develops a lightweight computer…
Multi-Object TrackingLearning Association via Track-Detection Matching for Multi-Object Tracking
Multi-object tracking aims to maintain object identities over time by associating detections across video frames. Two dominant paradigms exist in literature: tracking-by-detection methods, which are computationally effic…
Multi-Object TrackingLink PredictionMetric LearningTrackTeller: Temporal Multimodal 3D Grounding for Behavior-Dependent Object References
Understanding natural-language references to objects in dynamic 3D driving scenes is essential for interactive autonomous systems. In practice, many referring expressions describe targets through recent motion or short-t…
Multi-Object TrackingBeyond Proximity: A Keypoint-Trajectory Framework for Classifying Affiliative and Agonistic Social Networks in Dairy Cattle
Precision livestock farming requires objective assessment of social behavior to support herd welfare monitoring, yet most existing approaches infer interactions using static proximity thresholds that cannot distinguish a…
Multi-Object TrackingObject DetectionTUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
In Intelligent Transportation Systems (ITS), multi-object tracking is primarily based on frame-based cameras. However, these cameras tend to perform poorly under dim lighting and high-speed motion conditions. Event camer…
Multi-Object TrackingPedestrian DetectionEvent-based visionLeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping
High resolution phenotyping at the level of individual leaves offers fine-grained insights into plant development and stress responses. However, the full potential of accurate leaf tracking over time remains largely unex…
Multi-Object TrackingMeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation
This paper proposes a large-scale multi-modal dataset for referring motion expression video segmentation, focusing on segmenting and tracking target objects in videos based on language description of objects' motions. Ex…
Referring Video Object SegmentationMulti-Object TrackingVideo SegmentationVideo CaptioningGorillaWatch: An Automated System for In-the-Wild Gorilla Re-Identification and Population Monitoring
Monitoring critically endangered western lowland gorillas is currently hampered by the immense manual effort required to re-identify individuals from vast archives of camera trap footage. The primary obstacle to automati…
Multi-Object Tracking