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/fanshixiong/tracker
https://github.com/feizc/Object-Detection-Pytorch
https://github.com/fengyang95/pyCFTrackers
https://github.com/fepegar/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/fgabel/EfficientDet-DeepSORT-Tracker
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/filippogiruzzi/semantic_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/fnando1995/INTEL_Project
https://github.com/foolwood/RASNet
https://github.com/foolwood/SiamMask
Fast Online Object Tracking and Segmentation: A Unifying Approach
https://github.com/freeniliang/caffe-ssd
https://github.com/frgfm/Holocron
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/fyumoto/EIF
https://github.com/galprz/brain-tumor-segemntation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/galprz/brain-tumor-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ganeshpawar95/removebg1
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/georgeyiasemis/U-Net-PyTorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/gerardrbentley/Pytorch-U-Net-AutoEncoder
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/gmatasci/SemanticSegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/gmt710/caffe-ssd
https://github.com/gmt710/caffe_gpu
https://github.com/godcrampy/re3-custom
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/goksenin-uav/strongsort-pip
https://github.com/gomezzz/distmsmatch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/google-deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/google-deepmind/perception_test
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
https://github.com/google-research/deeplab2
https://github.com/google-research/localmot
https://github.com/gorjida/ap-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/got-10k/toolkit-matlab
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
https://github.com/goyalyash1224/Nuclei-detection-and-segmentaion
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/greatsharma/DeepLearning-Papers-Implementation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/gregbugaj/unet-reference-impl
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/grossjohannes/AlignNet-3D
AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects
https://github.com/gsan2/FairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/guanfuchen/deep_sort
Simple Online and Realtime Tracking with a Deep Association Metric
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.