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/linspace100/AI_Traffic_Violation_Detector
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linspace100/AI_Traffic_Violence_Detector
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linspace100/AI_traffic_police
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/linzhi123/DCFNet
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
https://github.com/linzhi123/DCFNet-pytouch
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
https://github.com/liqiang2018/Vehicle_delector
https://github.com/liqiang2018/ssd_mobilenet1.5
https://github.com/liuky74/caffe_liuky74
https://github.com/liuliu408/caffe-ssd
https://github.com/livox-sdk/livox_detection
https://github.com/ljllili23/unet_baseline
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/logiklesuraj/SiamFC
https://github.com/logiklesuraj/siamfcex
https://github.com/lrr-rachel/PCDUNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ls-da3m0ns/DeepSort_with_yolov5
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/ls-da3m0ns/Multiple-Object-Tracking
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/lswzjuer/caffe-adas
https://github.com/ltracegeo/deep-mineralogical-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lufficc/SSD
https://github.com/lurenhaothu/CWMI
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lvsn/6DOF_tracking_evaluation
https://github.com/lyffly/Segmentation_with_my_dataset
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lyqcom/2D-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lyqcom/deepsort
https://github.com/lzx1413/CAFFE_SSD
https://github.com/mHealthBuet/SegCodeNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/macomino/TFM
https://github.com/manideep2510/eye-in-the-sky
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/manishsoni1908/mobilenet-ssd-keras
https://github.com/map-learning/tl2la
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/mapeAAU/3D-ZeF
https://github.com/mariontan/YoloObjectTrack
https://github.com/mariosfourn/ScienceBowl
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/martin-danelljan/ECO
https://github.com/massucattoj/unet-model
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.