Papers Object Tracking
“Object Tracking” 태그가 달린 논문 2,087편 · 필터 해제
CHOTA: A Higher Order Accuracy Metric for Cell Tracking
The evaluation of cell tracking results steers the development of tracking methods, significantly impacting biomedical research. This is quantitatively achieved by means of evaluation metrics. Unfortunately, current metr…
Cell TrackingMultiple Object TrackingObject TrackingLow-Light Object Tracking: A Benchmark
In recent years, the field of visual tracking has made significant progress with the application of large-scale training datasets. These datasets have supported the development of sophisticated algorithms, enhancing the …
ObjectObject TrackingVisual Object TrackingVisual TrackingMambaEVT: Event Stream based Visual Object Tracking using State Space Model
Event camera-based visual tracking has drawn more and more attention in recent years due to the unique imaging principle and advantages of low energy consumption, high dynamic range, and dense temporal resolution. Curren…
MambaObject LocalizationObject TrackingVisual Object Tracking+1GSLAMOT: A Tracklet and Query Graph-based Simultaneous Locating, Mapping, and Multiple Object Tracking System
For interacting with mobile objects in unfamiliar environments, simultaneously locating, mapping, and tracking the 3D poses of multiple objects are crucially required. This paper proposes a Tracklet Graph and Query Graph…
Multiple Object TrackingObjectobject-detectionObject Detection+1MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model
Tracking by detection has been the prevailing paradigm in the field of Multi-object Tracking (MOT). These methods typically rely on the Kalman Filter to estimate the future locations of objects, assuming linear object mo…
Mambamotion predictionMulti-Object TrackingMultiple Object Tracking+2RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking
Data association is an essential part in the tracking-by-detection based Multi-Object Tracking (MOT). Most trackers focus on how to design a better data association strategy to improve the tracking performance. The rule-…
Edge ClassificationMulti-Object TrackingObject TrackingPanacea+: Panoramic and Controllable Video Generation for Autonomous Driving
The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework for generating video data in driving sc…
3D Object Detection3D Object TrackingAutonomous DrivingLane Detection+6Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters
We present a novel filtering algorithm that employs Bayesian transfer learning to address the challenges posed by mismatched intensity of the noise in a pair of sensors, each of which tracks an object using a nonlinear d…
Object TrackingTransfer LearningFruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images capture…
Object TrackingOptical Flow EstimationVoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network. Despite demonstrated success, such networks suffer from some fundamental problems: 1) It conta…
3D Single Object TrackingGPUObject TrackingTask 23D Single-object Tracking in Point Clouds with High Temporal Variation
The high temporal variation of the point clouds is the key challenge of 3D single-object tracking (3D SOT). Existing approaches rely on the assumption that the shape variation of the point clouds and the motion of the ob…
3D Single Object TrackingObject TrackingSiamMo: Siamese Motion-Centric 3D Object Tracking
Current 3D single object tracking methods primarily rely on the Siamese matching-based paradigm, which struggles with textureless and incomplete LiDAR point clouds. Conversely, the motion-centric paradigm avoids appearan…
3D Object Tracking3D Single Object TrackingMotion EstimationObject+1Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach
The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple …
cross-modal alignmentMultiple Object TrackingObjectobject-detection+4Medical SAM 2: Segment medical images as video via Segment Anything Model 2
Medical image segmentation plays a pivotal role in clinical diagnostics and treatment planning, yet existing models often face challenges in generalization and in handling both 2D and 3D data uniformly. In this paper, we…
Image SegmentationInteractive SegmentationMedical Image SegmentationObject Tracking+4A Batch Update Using Multiplicative Noise Modelling for Extended Object Tracking
While the tracking of multiple extended targets demands for sophisticated algorithms to handle the high complexity inherent to the task, it also requires low runtime for online execution in real-world scenarios. In this …
Object TrackingSharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis
Elasmobranchs (shark sand rays) represent a critical component of marine ecosystems. Yet, they are experiencing global population declines and effective monitoring of populations is essential to their protection. Underwa…
Multi-Object TrackingObject TrackingSandMEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset
In this data article, we introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset. This dataset provides a synchronized stream of event data and grayscale images of traffic scenes, captured us…
Event-based visionObjectobject-detectionObject Detection+2Progressive Domain Adaptation for Thermal Infrared Object Tracking
Due to the lack of large-scale labeled Thermal InfraRed (TIR) training datasets, most existing TIR trackers are trained directly on RGB datasets. However, tracking methods trained on RGB datasets suffer a significant dro…
Domain AdaptationObjectObject TrackingSubdomain adaptation+1Leveraging Foundation Models via Knowledge Distillation in Multi-Object Tracking: Distilling DINOv2 Features to FairMOT
Multiple Object Tracking (MOT) is a computer vision task that has been employed in a variety of sectors. Some common limitations in MOT are varying object appearances, occlusions, or crowded scenes. To address these chal…
Knowledge DistillationMulti-Object TrackingMultiple Object TrackingObject TrackingMultiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis
Panoramic cycling videos can record 360{\deg} views around the cyclists. Thus, it is essential to conduct automatic road user analysis on them using computer vision models to provide data for studies on cycling safety. H…
Multiple Object Trackingobject-detectionObject DetectionObject Tracking