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/RashadGarayev/OpencvDnnmodule
https://github.com/RentadroneCL/Photovoltaic_Fault_Detector
https://github.com/RentadroneCL/model-definition
https://github.com/Richard-cpu2333/tx2dl
https://github.com/Robotmurlock/Motrack
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/RunzhaoHuang/DeepSort_YOLOV5_OnScreen
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/RvI101/Ball-Action-Recognition
https://github.com/RvI101/DeepBall-Keras
https://github.com/SK124/Semantic-Segmentaion-Using-UNets
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
https://github.com/STVIR/pysot
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/SahinTiryaki/Brain-tumor-segmentation-Vgg19UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SarthakGarg13/Severstal-Steel-Defect-Detection
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Scienceseb/Input-Dropout-for-Spatially-Aligned-Modalities
https://github.com/Sentient07/HeadHunter-T
https://github.com/SeonbeomKim/TensorFlow-pix2pix
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SfTI-Robotics/ROS-label-node
https://github.com/ShahedSabab/imagePrep
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ShaileshKumar97/Speech-Enhancement
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SharadGitHub/OctaveUnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Sharut/CT_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ShivamPrajapati2001/People_Counter
https://github.com/ShobhitMaheshwari/sign-language1
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ShuaiBai623/MFT
Multi-hierarchical Independent Correlation Filters for Visual Tracking
https://github.com/Shumway82/U-Net-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SilvioGiancola/TrackingNet-devkit
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
https://github.com/Simurgh818/UCSF_QBI_Hackathon_ImageSegmentor
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Singh07-Shubham/pytorch-object-detection
https://github.com/SixQuant/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SkalskiP/make-sense
https://github.com/Sobeit-Tim/NuguEyeTest
https://github.com/SonginCV/GMPHD_MAF
https://github.com/SonginCV/GMPHD_SAF
https://github.com/Soren-Kierkegaard/MRI-Image-Analysis
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.