https://github.com/FastTrackOrg/FastTrack
공식 코드FastTrack: an open-source software for tracking varying numbers of deformable objects
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Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
<span style="color:grey; opacity: 0.6">( Image credit: Towards-Realtime-MOT )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
FastTrack: an open-source software for tracking varying numbers of deformable objects
General Object Foundation Model for Images and Videos at Scale
TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking
Learning Data Association for Multi-Object Tracking using Only Coordinates
An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes
Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion
History-Aware Transformation of ReID Features for Multiple Object Tracking
Simultaneous Multi-View Camera Pose Estimation and Object Tracking with Square Planar Markers
HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization
Fast and Accurate Online Video Object Segmentation via Tracking Parts
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Rethinking the competition between detection and ReID in Multi-Object Tracking
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking
MPM: Joint Representation of Motion and Position Map for Cell Tracking
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions
CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
AiATrack: Attention in Attention for Transformer Visual Tracking
Rotation Adaptive Visual Object Tracking with Motion Consistency
Joint-task Self-supervised Learning for Temporal Correspondence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.