Papers with Code 아카이브
Pedestrian Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/tinyvision/SOLIDER
공식 코드https://github.com/tjubiit/TJU-DHD
공식 코드From Handcrafted to Deep Features for Pedestrian Detection: A Survey
https://github.com/tusimple/sst
공식 코드Embracing Single Stride 3D Object Detector with Sparse Transformer
https://github.com/wielgosz-info/carla-pedestrians
공식 코드https://github.com/wongkinyiu/yolov7
공식 코드YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/xiangbogaobarry/mambast
공식 코드MambaST: A Plug-and-Play Cross-Spectral Spatial-Temporal Fuser for Efficient Pedestrian Detection
https://github.com/xiaohaoChen/rrc_detection
공식 코드Accurate Single Stage Detector Using Recurrent Rolling Convolution
https://github.com/xinyuelxy/bias-behind-the-wheel-fairness-testing-of-autonomous-driving-systems
공식 코드Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
https://github.com/xjtlu-cvlab/3drom
공식 코드3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization
https://github.com/xuez-phd/tfdet
공식 코드https://github.com/yanglindeng/mmdrfuse
공식 코드MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusion
https://github.com/yangyanggirl/bos
공식 코드Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments
https://github.com/yinghuixing/ms-detr
공식 코드https://github.com/yueruchen/Pedestrian-Synthesis-GAN
공식 코드Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
https://github.com/zhaoweicai/mscnn
공식 코드A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
https://gitlab.com/haghdam/deep_active_learning
공식 코드https://gitlab.com/sustainable-continuum-monitoring/self-adaptive-moop
공식 코드An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
https://github.com/1512159/tf-faster-rcnn-medico
https://github.com/2023-MindSpore-1/ms-code-119
Exploring Visual Context for Weakly Supervised Person Search
https://github.com/2023-MindSpore-1/ms-code-120
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/FCOS
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet
https://github.com/2023-MindSpore-1/ms-code-44
https://github.com/2023-MindSpore-1/ms-code-59
https://github.com/2023-MindSpore-1/ms-code-60
https://github.com/2023-MindSpore-4/Code10/tree/main/FCOS
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaNet
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_yolov3
https://github.com/2023-MindSpore-4/Code16/tree/main/yanyichao/mindspore_CGPS-master
Exploring Visual Context for Weakly Supervised Person Search
https://github.com/2023-MindSpore-4/Code9/tree/main/YOLOv3
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50
https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.