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/nirajdpandey/Object-detection-and-localization-using-SSD
https://github.com/nithi89/unet_darknet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/niveditarahurkar/CS231N-ActionRecognition
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/nolanzzz/MTMCT
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/noornk/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/nope0124/ssd_keras_hisakawa
https://github.com/nota-github/AIC2024_Track1_Nota
https://github.com/nsom/ssd
https://github.com/nusneuromorphic/eTLD
https://github.com/nutintin/Robotic_SP2019
https://github.com/nvidia-ai-iot/lidar_ai_solution
https://github.com/nvlong21/person_Counting
https://github.com/oakleighw/BRATS-Pretext-Tasks-for-Self-supervised-Deep-Learning
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/oaqoe-DWQ/Yolov5_DeepSort_Pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/object-detection-algorithm/SSD
https://github.com/ofrikleinfeld/ObjectPermanence
https://github.com/ogatarina-sq/secyolov7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/oist/DenseObjectAnnotation
https://github.com/oist/DenseObjectDetection
https://github.com/oliverquintana/UnetSegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/oliverz17/Unet_Keras
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/oljikeboost/PlayerTracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/omkar13/MaskTrack
https://github.com/open-mmlab/mmaction2
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmdetection3d
https://github.com/open-mmlab/mmsegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/open-mmlab/mmyolo
https://github.com/opencv/openvino_training_extensions
https://github.com/opengvlab/videochat-r1
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
https://github.com/openvinotoolkit/training_extensions
https://github.com/orobix/retina-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/osilab-kaist/komet-benchmark-dataset
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/osmr/imgclsmob
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/overshiki/unet-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.