https://github.com/berenslab/retinal-vessel-segmentation-benchmark
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
Object Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 10
- 보존된 평가 기록
- 43
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/bespontaneous/ffn
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bespontaneous/mca-pytorch
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bfortuner/pytorch_tiramisu
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bharath3794/UNet-Image-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bingfengyan/visam
The 1st-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
https://github.com/birdman9390/MetaMaskTrack
https://github.com/birosjh/pytorch_ssd
https://github.com/birosjh/simple_ssd
https://github.com/biss/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/bleedingfight/caffe-env
https://github.com/bmoradi93/ssd-object-detection
https://github.com/bok9504/UAV_MOT_LineRegistration
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/bournes/kkxv
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/bowang-lab/medsam2
https://github.com/bowu1004/instance_segmentation_RealSense
https://github.com/bragancas/vnet_pytorch-atriaseg2018
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/branislavhesko/segmentation_framework
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/brdiep113/UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/brunasil98/u-net_Cancer
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/burakalperen/Pytorch-Semantic-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/buzkent86/EnKCF_Tracker
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
https://github.com/buzkent86/EnKCF_Tracking_WACV18
EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object Tracking
https://github.com/c1ph3rr/U-Net-Convolutional-Networks-For-Biomedicalimage-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cabeen/neu-net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/carinanorre/Brain-Tumour-Segmentation-Dissertation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/casys-kaist/cova
https://github.com/cds-mipt/cds-tracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/cdulhanty/syde672
https://github.com/cedriclmenard/irislandmarks.pytorch
Real-time Pupil Tracking from Monocular Video for Digital Puppetry
https://github.com/celinechanny/FV-UPatches
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cfdoge/w9ai
https://github.com/cfotache/pytorch_objectdetecttrack
https://github.com/chakerouari/UNET_segmetation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/championway/re3_tracking
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.