https://github.com/danielenricocahall/Keras-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
Object Tracking
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/danielenricocahall/Keras-Weighted-Hausdorff-Distance-Loss
https://github.com/danielgordon10/re3-tensorflow
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/danielzgsilva/MOT
https://github.com/dannofield/CarND-Extended-Kalman-Filter
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/datacrisis/BOBBY2
https://github.com/david-cortes/isotree
https://github.com/daxiaHuang/brainstorm
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dbailey00/Semantic-Segmentation-With-Seismic-Images
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dcnieho/segment-anything-2
https://github.com/debjani-bhowmick/harmonic-net-pytorch
https://github.com/decentNick/Signals_big_lab_SPBSTU
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/derek285/yolov4_deep_sort_pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/deshumake/SORT
https://github.com/devanshpratapsingh/U-net_for_Multiclass_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dhu-mmct/dhu-mmct
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/dingwoai/FairMOT-BDD100K
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/divamgupta/image-segmentation-keras
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo/smot
https://github.com/dnozza/profanity-obfuscation
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/dry01/segmentation-
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dsandeep0138/UNet-Colorization
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dscv-lab/KMF2JMTtracking
Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
https://github.com/dugusiqing/caffe
https://github.com/dvl-tum/mot_neural_solver
https://github.com/dvl-tum/motsynth-baselines
https://github.com/dvlab-research/3d-box-segment-anything
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
https://github.com/e9190712/vessel_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/eddiebarry/SingleShotDetector
https://github.com/ethanhou99/ml_learn
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ethvis/qd-track
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
https://github.com/ezelikman/anonymal
Fast Online Object Tracking and Segmentation: A Unifying Approach
https://github.com/facebookresearch/DetectAndTrack
https://github.com/facebookresearch/segment-anything
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.