https://github.com/wjcheon/MedicalImageSegmentation_Pytorch
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
- 10
- 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/wk738126046/deploy
https://github.com/wlstyql/keras_with_mat_for_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/wmuron/motpy
https://github.com/work-mohit/UNet-from-scratch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/wuhengliangliang/ByteTrack
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
https://github.com/wy-moonind/trackrcnn_with_deepsort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/xTRam1/Object-Detection-on-Custom-Dataset
https://github.com/xahidbuffon/SUIM
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/xahidbuffon/SVAM-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/xamyzhao/brainstorm
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/xf-zh/InScope
https://github.com/xiaohaoChen/rrc_detection
https://github.com/xiaoxiong74/Object-Detection-and-Tracking
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/xiey1/Nucleus_detection
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/xifen523/cod
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving
https://github.com/xingpingdong/CLNet-tracking
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/xinli-zn/informative-tracking-benchmark
Object Tracking by Jointly Exploiting Frame and Event Domain
https://github.com/xinwei666/mmgenerativeir
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/xiuyu0000/new_papers_codes/tree/main/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/new_papers_codes/tree/main/OSVOS
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
https://github.com/xiuyu0000/papers_with_examples/tree/main/case_10_ssd300_mobilenetv2_voc2012_detection
https://github.com/xiuyu0000/vision/blob/main/mindvision/msdetection/models/ssd.py
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xkhoanamx1/lung_crop_xray
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/xr-lee/neural-symbolic
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking
https://github.com/xuduo35/STAPLE
https://github.com/xuefeng-zhu5/cdaat
https://github.com/xyfer17/ssd-caffe
https://github.com/xzgz/caffe-ssd
https://github.com/yahoo/spivak
https://github.com/yangyucheng000/Paper-4/tree/main/ProC-KD-main
ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/yangyucheng000/University/tree/main/model-3/sam
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2
https://github.com/yangyucheng000/papercode-2/tree/main/ProC-KD-main
ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/yaohungt/GSTEG_CVPR_2019
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.