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Pedestrian Detection

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하위 태스크: 1
평가표: 9
보존된 평가 기록: 92

Pedestrian detection is the task of detecting pedestrians from a camera.

Further state-of-the-art results (e.g. on the KITTI dataset) can be found at 3D Object Detection.

<span style="color:grey; opacity: 0.6">( Image credit: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/anookeen/yolo

Focal Loss for Dense Object Detection

https://github.com/antonoeschmidt/darknet

YOLOv3: An Incremental Improvement

https://github.com/aotumanbiu/awesome-object-detection

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/areberoto/darknet-thesis

YOLOv3: An Incremental Improvement

https://github.com/artemmavrin/focal-loss

Focal Loss for Dense Object Detection

https://github.com/artxtech/darknet-rnn

Focal Loss for Dense Object Detection

https://github.com/artynet/darknet-alexeyAB

Focal Loss for Dense Object Detection

https://github.com/ayushpalak/darknet_cokebottles

Focal Loss for Dense Object Detection

https://github.com/ayushpalak/darknet_multi_cokebottles

Focal Loss for Dense Object Detection

https://github.com/bailvwangzi/repulsion_loss_ssd

Repulsion Loss: Detecting Pedestrians in a Crowd

https://github.com/banben/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/bareblackfoot/faster-rcnn.selection

Feature Pyramid Networks for Object Detection

https://github.com/bareblackfoot/lddp-tf-faster-rcnn

Feature Pyramid Networks for Object Detection

https://github.com/basaltzhang/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/benihime91/pytorch_retinanet

Feature Pyramid Networks for Object Detection

https://github.com/benjamintli/darknet-gun-detector

YOLOv3: An Incremental Improvement

https://github.com/benjamintli/training-results-azure

YOLOv3: An Incremental Improvement

https://github.com/bharatsush/object_detection

Focal Loss for Dense Object Detection

https://github.com/bimsarapathiraja/mccl

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/binhdv92/darknet

Focal Loss for Dense Object Detection

https://github.com/binhdv92/darknet_kriyeng

Focal Loss for Dense Object Detection

https://github.com/blueardour/AdelaiDet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/bobby20180331/darknet_pycharm

Focal Loss for Dense Object Detection

https://github.com/bowensong0312/darknet

YOLOv3: An Incremental Improvement

https://github.com/buaaYYC/faster-rcnn-tensorflow

Feature Pyramid Networks for Object Detection

https://github.com/buptdbj/darknet-windows-linux

Focal Loss for Dense Object Detection

https://github.com/caihaocong/Yolov3_AlexeyAB_Version

Focal Loss for Dense Object Detection

https://github.com/calmisential/YOLOv3_TensorFlow2

YOLOv3: An Incremental Improvement

https://github.com/cameronchoi/r3det-copy

Focal Loss for Dense Object Detection

https://github.com/ccchang1023/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/chencq1234/maskrcnn_facebook

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/chengche6230/rest

Graph Neural Networks for Cross-Camera Data Association

https://github.com/chengzhengxin/groupsoftmax-simpledet

Feature Pyramid Networks for Object Detection

https://github.com/chenwuperth/ClaRAN

Feature Pyramid Networks for Object Detection

https://github.com/chincharles/u-emo

YOLOv3: An Incremental Improvement

https://github.com/citbrains/darknet_yolov4

YOLOv3: An Incremental Improvement

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.