Papers Multiple Object Tracking
“Multiple Object Tracking” 태그가 달린 논문 325편 · 필터 해제
When Fish Look Alike: Tracking Identities with Dual-branch Elasticity
Tracking dense, homogeneous targets like schooling fish remains a major challenge for multiple object tracking due to extreme inter-individual homogeneity, severe physical clustering, and rapid non-rigid deformations. Wh…
Multiple Object TrackingDual-level Adaptation for Multi-Object Tracking: Building Test-Time Calibration from Experience and Intuition
Multiple Object Tracking (MOT) has long been a fundamental task in computer vision, with broad applications in various real-world scenarios. However, due to distribution shifts in appearance, motion pattern, and catagory…
Multiple Object TrackingMulti-Object TrackingTest-time AdaptationFutrTrack: A Camera-LiDAR Fusion Transformer for 3D Multiple Object Tracking
We propose FutrTrack, a modular camera-LiDAR multi-object tracking framework that builds on existing 3D detectors by introducing a transformer-based smoother and a fusion-driven tracker. Inspired by query-based tracking …
Multiple Object TrackingMulti-Object TrackingMulti-tracklet Tracking for Generic Targets with Adaptive Detection Clustering
Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing metho…
Multiple Object TrackingAn Angular-Temporal Interaction Network for Light Field Object Tracking in Low-Light Scenes
High-quality 4D light field representation with efficient angular feature modeling is crucial for scene perception, as it can provide discriminative spatial-angular cues to identify moving targets. However, recent develo…
Multiple Object TrackingTemporal Misalignment Attacks against Multimodal Perception in Autonomous Driving
Multimodal fusion (MMF) plays a critical role in the perception of autonomous driving, which primarily fuses camera and LiDAR streams for a comprehensive and efficient scene understanding. However, its strict reliance on…
Multiple Object TrackingScene UnderstandingAutonomous DrivingObject DetectionWhen Trackers Date Fish: A Benchmark and Framework for Underwater Multiple Fish Tracking
Multiple object tracking (MOT) technology has made significant progress in terrestrial applications, but underwater tracking scenarios remain underexplored despite their importance to marine ecology and aquaculture. In t…
Multiple Object TrackingLearning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for such challenging scenes is performance-cri…
3D Pedestrian TrackingMultiple Object TrackingObject TrackingLiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking
Multi-object tracking from LiDAR point clouds presents unique challenges due to the sparse and irregular nature of the data, compounded by the need for temporal coherence across frames. Traditional tracking systems often…
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingUsing Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking
Head detection and tracking are essential for downstream tasks, but current methods often require large computational budgets, which increase latencies and ties up resources (e.g., processors, memory, and bandwidth). To …
Head DetectionMultiple Object TrackingObject TrackingTowards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking
This work addresses the critical lack of precision in state estimation in the Kalman filter for 3D multi-object tracking (MOT) and the ongoing challenge of selecting the appropriate motion model. Existing literature comm…
3D Multi-Object TrackingMulti-Object TrackingMultiple Object TrackingNavigate+5History-Aware Transformation of ReID Features for Multiple Object Tracking
The aim of multiple object tracking (MOT) is to detect all objects in a video and bind them into multiple trajectories. Generally, this process is carried out in two steps: detecting objects and associating them across f…
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingOVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer
Open-vocabulary multiple object tracking aims to generalize trackers to unseen categories during training, enabling their application across a variety of real-world scenarios. However, the existing open-vocabulary tracke…
Decodermultimodal interactionMultiple Object TrackingMultiple Object Tracking with Transformer+1INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy
In this work, we present INTACT, a novel two-phase framework designed to enhance the robustness of deep neural networks (DNNs) against noisy LiDAR data in safety-critical perception tasks. INTACT combines meta-learning w…
Meta-LearningMultiple Object TrackingObject TrackingHeterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos
Tracking multiple tiny objects is highly challenging due to their weak appearance and limited features. Existing multi-object tracking algorithms generally focus on single-modality scenes, and overlook the complementary …
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingA2VIS: Amodal-Aware Approach to Video Instance Segmentation
Handling occlusion remains a significant challenge for video instance-level tasks like Multiple Object Tracking (MOT) and Video Instance Segmentation (VIS). In this paper, we propose a novel framework, Amodal-Aware Video…
Instance SegmentationMultiple Object TrackingObjectObject Tracking+3Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity
Multiple Object Tracking (MOT) in thermal imaging presents unique challenges due to the lack of visual features and the complexity of motion patterns. This paper introduces an innovative approach to improve MOT in the th…
Multiple Object TrackingObjectObject TrackingGTA: Global Tracklet Association for Multi-Object Tracking in Sports
Multi-object tracking in sports scenarios has become one of the focal points in computer vision, experiencing significant advancements through the integration of deep learning techniques. Despite these breakthroughs, cha…
Multi-Object TrackingMultiple Object TrackingObject TrackingSIRA: Scalable Inter-frame Relation and Association for Radar Perception
Conventional radar feature extraction faces limitations due to low spatial resolution, noise, multipath reflection, the presence of ghost targets, and motion blur. Such limitations can be exacerbated by nonlinear object …
Multiple Object TrackingObjectobject-detectionObject Detection+4Is Multiple Object Tracking a Matter of Specialization?
End-to-end transformer-based trackers have achieved remarkable performance on most human-related datasets. However, training these trackers in heterogeneous scenarios poses significant challenges, including negative inte…
AttributeDomain GeneralizationMultiple Object TrackingObject+3