Papers with Code 아카이브
Pedestrian Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/MindSpore-scientific-2/code-10/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific-2/code-11/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific-2/code-11/tree/main/mgan-mindspore
Mask-Guided Attention Network for Occluded Pedestrian Detection
https://github.com/MindSpore-scientific-2/code-8/tree/main/STCrowd
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
https://github.com/MindSpore-scientific/code-10/tree/main/mgan-mindspore
Mask-Guided Attention Network for Occluded Pedestrian Detection
https://github.com/MindSpore-scientific/code-2/tree/main/ssFPN
https://github.com/Miracle1991/DetectionHub
https://github.com/NBvinay/vinb-ghost-car
https://github.com/NVIDIA/retinanet-examples
https://github.com/NomiMalik0207/PedSynth-and-PedGNN-for-Pedestrian-Intention-Prediction
https://github.com/NovasMax/R3Det-Refined-Single-Stage-Detector-with-Feature-Refinement-for-RO
https://github.com/NovasMax/yolov3
https://github.com/OFRIN/Tensorflow_FCOS
https://github.com/OFRIN/Tensorflow_RetinaFace
https://github.com/OlafenwaMoses/FireNET
https://github.com/OpenCv30/Yolov3
https://github.com/Oussamayousre/Yoga_pose_recognizer
https://github.com/PIlotcnc/neural
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleOCR
https://github.com/PaddlePaddle/PaddleYOLO
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/PaiHsuehChung/DockerYOLOV3
https://github.com/PengchengAi/tf-faster-rcnn-pcai
https://github.com/Pengchengzhi/Yolov3_Own-dataset
https://github.com/Pepitaw/darknet
https://github.com/PericlesPet/darknet-demo
https://github.com/PeterTKovacs/zold137
https://github.com/PetersonSilvadeJesus/yolov3-data-images
https://github.com/PolarisAlpha/darknet
https://github.com/PolinaYA/yolov3
https://github.com/Popoooo/darknet_test
https://github.com/Purkialo/CrowdDet
Detection in Crowded Scenes: One Proposal, Multiple Predictions
https://github.com/Pxtri2156/AdelaiDet_v2
https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu
https://github.com/Qengineering/YoloV3-ncnn-Jetson-Nano
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.