https://github.com/yassineAlouini/data-science-bowl-2018
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
Object Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yassouali/pytorch_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/yastrebksv/TrackNet
TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
https://github.com/yczhang1017/SSD_resnet_pytorch
https://github.com/yelantingfeng/pyLucid
https://github.com/yeqiuyi/caffe-ssd
https://github.com/yihengzhang-cv/mcl-motion-focused-contrastive-learning
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
https://github.com/yiranvang/TransCenter_official
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
https://github.com/youngbin-ro/Homography-based-MOTDT
https://github.com/youngbin-ro/MOTDT-original
https://github.com/youngsean/unseenobjectswithmeanshift
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
https://github.com/yoxu515/aot-benchmark
Associating Objects with Transformers for Video Object Segmentation
https://github.com/yqyao/ssd.pytorch
https://github.com/yuhsijen/Object-Detector
https://github.com/yuhsuanlin23130/Vehicle-Detection-2019
https://github.com/yujiang0922/Re-ID-Tracking
https://github.com/yumaloop/CV_report2019
https://github.com/yumaloop/kaggle-tgs-salt-competition
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/yuqj1990/caffe_deeplearning_train
https://github.com/yuqj1990/caffe_train
https://github.com/yuqj1990/deepano_face_train
https://github.com/yuqj1990/deepano_train
https://github.com/yuqj1990/face_train
https://github.com/zad1206/simtrack-mindspore
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
https://github.com/zakroum-hicham/gta-DeepEIou-YOLO
Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports
https://github.com/zamnius/uNet-seg-decathlon
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/zampie/Animation-standing-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/zampie/unet-colorization-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/zarif101/blood_cell_segmentation_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/zengwbz/Face-Tracking-usingFairMOT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/zfw1226/gym-unrealcv
Towards Active Vision for Action Localization with Reactive Control and Predictive Learning
https://github.com/zh320/medical-segmentation-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/zhanghuiyao/yolov7_mindspore
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/zhangxiutao/ROLO
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
https://github.com/zhiggins11/Semantic-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/zhixuhao/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.