https://github.com/tatigabru/kaggle-lyft
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/tatsuyaokunaga/Simpsons_Detection
https://github.com/tchernitski/caffe.deploy
https://github.com/tensorworksio/mot.cpp
https://github.com/terbed/Deep-rPPG
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks
https://github.com/ternaus/TernausNetV2
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ternaus/angiodysplasia-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/theAIGuysCode/yolov4-deepsort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/tianweiy/CenterPoint-KITTI
https://github.com/tiendatnguyen-vision/UNet-Image-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tolotrasamuel/real-time-object-detection-retraining-ncs-raspberry-pi
https://github.com/toseek01/eyebird
https://github.com/touchjet/re3-tensorflow
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/towhee-io/towhee/tree/main/towhee/models/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/trichtu/ConvLSTM-RAU-net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/trichtu/Recurrent_Attention_U_net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/trupewate/lung_segmentation_tutorial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tryolabs/luminoth
https://github.com/tryolabs/norfair
https://github.com/trypag/pytorch-unet-segnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tsingqguo/AttackTracker
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
https://github.com/tsixta/jnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tsunekoromochi/ssd
https://github.com/tusen-ai/simpletrack
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
https://github.com/tusimple/sst
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels
https://github.com/twairball/pyTracking
https://github.com/ubamba98/Brain-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ucbdrive/3d-vehicle-tracking
https://github.com/ucuapps/WSMIS
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/udacity/MLND-CN-Capstone-TGSImage
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ugent-korea/pytorch-unet-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/upc-postgrads/BrainScan
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/usnistgov/semantic-segmentation-fc-densenet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/usnistgov/semantic-segmentation-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/usnistgov/small-data-cnns
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/usuyama/pytorch-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.