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/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JanOPedersen/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JaryHuang/awesome_SSD_FPN_GIoU
https://github.com/Jee-King/ICCV2021_Event_Frame_Tracking
Object Tracking by Jointly Exploiting Frame and Event Domain
https://github.com/JeffCHEN2017/WSSTG
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/Jeongseungwoo/U_net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JifeiWang-WHU/Pytorch_Building_extraction
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JimmyLauren/caffe_ssd_mofified
https://github.com/JisuHann/MEME-Tracking
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/JisuHann/Object-Tracking
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/Jo-dsa/SemanticSeg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JoegameZhou/ssd
https://github.com/JoelPendleton/Edge-Detection-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JoelPendleton/Object-Contour-Detection-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/John1liu/YOLOV5-DeepSORT-Vehicle-Tracking-Master
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/JonathonLuiten/TrackEval
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
https://github.com/JoseVillagranE/SiameseNetworks
https://github.com/JosephPB/XNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JoyHsiao/Haitec
https://github.com/JrkJamie/eagermot_ms
https://github.com/JunweiLiang/Object_Detection_Tracking
https://github.com/K-Mike/Automatic-salt-deposits-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KARAASLAN-AI/re3-object-tracking
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/KUR-creative/SickZil-Machine
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KarthikDulam/U-NetCNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KeyG518/Unet_Cell_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KingGugu/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Kissonis/Quantum_test
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KostadinovShalon/UAVDetectionTrackingBenchmark
https://github.com/LKLQQ/Deepsort
https://github.com/LKLQQ/JDE
https://github.com/LKLQQ/tracktor
https://github.com/LanqingLi/Chromosome
https://github.com/Launch-on-Titania/DynOPETs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.