Papers Multiple Object Tracking
“Multiple Object Tracking” 태그가 달린 논문 326편 · 필터 해제
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with …
motion predictionMulti-Object TrackingMultiple Object TrackingObject+1Z-GMOT: Zero-shot Generic Multiple Object Tracking
Despite recent significant progress, Multi-Object Tracking (MOT) faces limitations such as reliance on prior knowledge and predefined categories and struggles with unseen objects. To address these issues, Generic Multipl…
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingLinear Object Detection in Document Images using Multiple Object Tracking
Linear objects convey substantial information about document structure, but are challenging to detect accurately because of degradation (curved, erased) or decoration (doubled, dashed). Many approaches can recover some v…
Instance SegmentationMultiple Object TrackingObjectobject-detection+4Type-to-Track: Retrieve Any Object via Prompt-based Tracking
One of the recent trends in vision problems is to use natural language captions to describe the objects of interest. This approach can overcome some limitations of traditional methods that rely on bounding boxes or categ…
Grounded Multiple Object TrackingMultiple Object TrackingObject TrackingTensor DecompositionS$^3$Track: Self-supervised Tracking with Soft Assignment Flow
In this work, we study self-supervised multiple object tracking without using any video-level association labels. We propose to cast the problem of multiple object tracking as learning the frame-wise associations between…
Multiple Object TrackingObjectObject TrackingOptical Flow EstimationA Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing
Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this paper, we present a feasibility s…
Edge-computingHuman DetectionIndoor LocalizationMulti-Person Pose Estimation+4OVTrack: Open-Vocabulary Multiple Object Tracking
The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional multiple object tracking (MOT) benchmarks…
DenoisingHallucinationKnowledge DistillationMulti-Object Tracking+3Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approach…
Multiple Object Trackingobject-detectionObject DetectionObject TrackingSportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the dom…
Multi-Object TrackingMultiple Object TrackingObjectObject TrackingStreaming Video Model
Video understanding tasks have traditionally been modeled by two separate architectures, specially tailored for two distinct tasks. Sequence-based video tasks, such as action recognition, use a video backbone to directly…
Action RecognitionDecodermodelMultiple Object Tracking+2Learnable Graph Matching: A Practical Paradigm for Data Association
Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly igno…
Graph MatchingGraph Neural NetworkMultiple Object TrackingObject Tracking+1Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, predictio…
Autonomous VehiclesConformal Predictionmotion predictionMulti-Object Tracking+63D-POP - An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture
Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior by enabling researchers to track the poses and locations of freely moving animals without any marker attachment. Ho…
3D Pose EstimationAnimal Pose EstimationMultiple Object TrackingPose TrackingUniversal Instance Perception as Object Discovery and Retrieval
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independen…
Described Object DetectionGeneralized Referring Expression ComprehensionInstance SegmentationMulti-Object Tracking and Segmentation+16Virtual Sparse Convolution for Multimodal 3D Object Detection
Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, int…
3D Multi-Object Tracking3D Object DetectionDepth CompletionMultiple Object Tracking+4Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation
Discriminative representation is essential to keep a unique identifier for each target in Multiple object tracking (MOT). Some recent MOT methods extract features of the bounding box region or the center point as identit…
Multi-Object TrackingMultiple Object TrackingObject TrackingOnline Multi-Object TrackingSpatio-Temporal Point Process for Multiple Object Tracking
Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challenging task as noisy and confusing detection…
Multiple Object TrackingObjectObject TrackingTracking Multiple Deformable Objects in Egocentric Videos
Most existing multiple object tracking (MOT) methods that solely rely on appearance features struggle in tracking highly deformable objects. Other MOT methods that use motion clues to associate identities across fram…
DisentanglementMotion DisentanglementMulti-Object TrackingMultiple Object Tracking+1UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement
Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association. Existing methods apply the three-step or two-step paradigm to generate r…
Multiple Object Trackingobject-detectionObject DetectionObject TrackingTracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning
Unsupervised learning is a challenging task due to the lack of labels. Multiple Object Tracking (MOT), which inevitably suffers from mutual object interference, occlusion, etc., is even more difficult without label s…
Multiple Object TrackingObjectObject Tracking