Papers with Code 아카이브
Object Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 10
- 보존된 평가 기록
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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/chandra411/Product-Detection
https://github.com/chaowang1994/caffe-ssd
https://github.com/charlychiu/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cheind/py-motmetrics
https://github.com/chelvanai/Oneshot-Face-Identify
https://github.com/chenjian123456/caffe-ssd
https://github.com/chenjunweii/mxnet-ssd
https://github.com/chenyizi086/wu.2023.sigspatial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/chetanchawla/Sort-Tracker
https://github.com/chihyanghsu0805/object_detection_yolo
https://github.com/chikuanlin/re3-pytorch
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/chinakook/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/choiwb/Computer_Vision_for_Smart_Factory
https://github.com/chonyy/AI-basketball-analysis
https://github.com/chonyy/ML-auto-baseball-pitching-overlay
https://github.com/chowkamlee81/CentrePointNet
https://github.com/clarencenhuang/dl-colorize
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/clemkoa/u-net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cmf3673/spotti
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/cmougan/IsolationForest
https://github.com/code-implementation1/Code1/tree/main/Deepsort
https://github.com/code-implementation1/Code4/tree/main/JDE
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
https://github.com/code-implementation1/Code6/tree/main/OSVOS
https://github.com/code-implementation1/Code8/tree/main/SiamFC
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/code-implementation1/Code9/tree/main/SSD
https://github.com/code-implementation1/Code9/tree/main/nri-mpm
MPM: Joint Representation of Motion and Position Map for Cell Tracking
https://github.com/code-implementation1/Code9/tree/main/ocean_model
https://github.com/codecolony/re3-tensorflow
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/computational-cell-analytics/micro-sam
Trackastra: Transformer-based cell tracking for live-cell microscopy
https://github.com/condiff-dataset/ConDiff
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cosmic-cortex/pytorch-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cruiseresearchgroup/DroTrack
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
https://github.com/cvlab-stonybrook/HandLer
https://github.com/cyx2016/siamrpn_mindspore
https://github.com/daniansan/i3d_mindspore
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.