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/valentina-kustikova/dnn-object-detectors-comp
https://github.com/vamsi3/pix2pix
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vandangorade/BrandLOGO_detection
https://github.com/vankhoa21991/medicalImgSEg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vbelz/Speech-enhancement
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vdurnov/xview2_1st_place_solution
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vetlebh/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vg-sravan/Cycle-Consistent-Generative-Adversarial-Networks
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vhvkhoa/sort
https://github.com/victordibia/handtracking
https://github.com/vietanhdev/open-adas
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vijayvee/behavior-recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vijayvee/behavior_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/viplix3/BoTSORT-cpp
https://github.com/visaVita/mmaction2
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/visiblesensors/caffe-ssd
https://github.com/vislearn/libmpopt
https://github.com/vita-epfl/rock-pytorch
https://github.com/vivek20dadhich/deep-learning-object-detection
https://github.com/vlievin/Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vnbot2/object-locator
https://github.com/void-in-the-matrix/nuclei-segmentation-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vqdang/hover_net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/vqdang/xy_net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/wangdongdut/dut-anti-uav
https://github.com/wangkingkingking/ssd_mathocr
https://github.com/wangrui1996/caffe-ssd
https://github.com/wangxiao5791509/SiamDW_tracker_revised
Deeper and Wider Siamese Networks for Real-Time Visual Tracking
https://github.com/wangxiao5791509/Tracking-with-Deep-Reinforcement-Learning
https://github.com/wangyi111/international-archaeology-ai-challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/watarus0208/ssd_keras
https://github.com/wayne1204/NOAA-fish-finding
https://github.com/weidi1024/caffe-ssd-correct
https://github.com/wenyuqing/panacea
Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
https://github.com/wj1tr0y/SSD
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.