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/johschmidt42/pytorch-2d-3d-unet-tutorial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jonryf/deep-learning-image-colorization-using-gan
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jovenwayfarer/pneumonia
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jqueguiner/camembert-as-a-service
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jqueguiner/image-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jtiger958/pytorch-computer-vision-basic
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jtiger958/pytorch-computer-vision-tutorial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/juanluisrosaramos/deep-sort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/julia-bush/unet_TGS
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/junjieinkit/Towards-MOT
https://github.com/jvanvugt/pytorch-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kadirnar/bytetrack-pip
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
https://github.com/kadirnar/deepsort-pip
https://github.com/kadirnar/ocsort-pip
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
https://github.com/kadirnar/yolov5-strongsort
https://github.com/kadirnar/yolov7-pip
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/kaichoulyc/course_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kaland313/vitmav45-ShipSeakers
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kamkyu94/AdapTrack
https://github.com/kamkyu94/CMTrack
https://github.com/kamkyu94/TrackTrack
https://github.com/kanesoban/ssd
https://github.com/kangyiS/caffe_ky
https://github.com/kannyjyk/Nested-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/karolzak/keras-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kaustavsenkvs/DeepSort-Algorithm
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/kcang2/Prune_U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kdha0727/cancer-instance-segmentation-from-tissue
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kelvin-jose/UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ken-power/SelfDrivingCarND-ExtendedKalmanFilter
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/ken-power/SensorFusionND-3D-Object-Tracking
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/kfx7577/Caffe_VehicleCounter
https://github.com/khalidw/MOT16_Annotator
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
https://github.com/kibernetika-ai/re3-tensorflow
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/kilgore92/Probabalistic-U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.