https://github.com/guanfuchen/video_obj
Fast and Accurate Online Video Object Segmentation via Tracking Parts
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/gundeep15/SCD_AI_Project
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/guoxixu/TrafficFlowTracking
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
https://github.com/guptarohit994/ECE285_Graduate_Descent_SSD
https://github.com/gurkirt/AMTNet
https://github.com/gurkirt/realtime-action-detection
https://github.com/gvtulder/elasticdeform
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hamdaan19/NavGuide
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hampen2929/CenterTrack
https://github.com/haratzouvara/image_restoration
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/harishhirthi/Indian-Traffic-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/harsh-jadhav/SIIM-Pneumothorax-Case-Study2
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/harsh2912/people-tracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/hasanirtiza/PedesFormer-SwinTransformer-For-Pedestrian-Detection
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
https://github.com/hassony2/kinetics_i3d_pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hayashimasa/UNet-PyTorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/helloxy96/CS5242_Project2020
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hemanth346/mde_bs
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/henrytsui000/YOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/hidekazu300/test
https://github.com/highwaywu/tianchi-fft2
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hitsz-tmg/videovista
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
https://github.com/hkim1207/2021MIP
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/hoaitrinh459/RobotNhatBongTennis
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/holmescao/ants_marking_and_analysis_tools
https://github.com/hrlblab/map3d
Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images
https://github.com/htso/Pedestrian_in_few_shots
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/hutgun11/muti_object_tracking_re3
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/huytranvan2010/SSD
https://github.com/hycis/TensorGraph
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hydrogo/rainnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hyh21521038/ssd-plate_detection
https://github.com/hyunjoonbok/Unet-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/iarai/landslide4sense-2022
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/iarai/weather4cast
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ibaiGorordo/ONNX-SAM2-Segment-Anything
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.