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csrnet-crowd-detection's Introduction

CSRNet-Crowd-Detection

Table of Content

generals

https://supperundsupper.com/en/usecases/crowd-control-using-artificial-intelligence

https://www.sciencedirect.com/science/article/pii/S187705092300220X = crowd counting

https://en.yna.co.kr/view/AEN20231102007200315 = ai crowd monitoring seoul subway

https://intelgic.com/insights/crowd-analysis-using-vision-ai-5-trends-to-watch-in-2024/

Csrnet_algorithms

https://github.com/ZhengPeng7/CSRNet-Keras/tree/master = csrnet-keras

https://github.com/Neerajj9/CSRNet-keras

https://github.com/imenebak/UAV-Crowd-Counting?tab=readme-ov-file = uav crowd counting

https://github.com/husainattar/Rail-Rush

https://github.com/tempdata73/crowd_analysis

https://github.com/leeyeehoo/CSRNet?tab=readme-ov-file = model,dataset

https://github.com/hcongvan/Crowed-Counting

https://github.com/poojavinod100/People-Counting-Crowd-Density-Detection/tree/master = crowd density detection

https://github.com/Tikam02/CrowdEstimation/tree/master = crowd estimation

https://github.com/gjy3035/Awesome-Crowd-Counting?tab=readme-ov-file#LA-Batch = general

https://github.com/wk910930/crowd_density_segmentation?tab=readme-ov-file = crowd density segmentation

https://github.com/katnoria/crowd-density/tree/master = crowd density

etc: ultralytics/yolov5#1779 yolo for crowd detection

  • kaggle

https://www.kaggle.com/code/weijiehung/csrnet

https://www.kaggle.com/code/tthien/csrnet-tf-shanghaitech-v9/notebook

https://www.kaggle.com/search?q=CSRnet search results

CSRNet_Study

  • papers:

https://arxiv.org/pdf/1802.10062.pdf = dialeted CNN

https://arxiv.org/pdf/1707.09605.pdf = CNN, cascaded multi task learning of high density estimation

https://arxiv.org/pdf/1912.03677.pdf = crowd counting, image translation

https://arxiv.org/pdf/2003.00217.pdf = NAS-count, counting by density

https://link.springer.com/article/10.1007/s11042-020-10035-z = psnr, ssim

  • blogs:

https://medium.com/@kaamyakpant_67666/building-a-crowd-counting-model-using-deep-learning-e8b8e925674e = crowd counting model

https://medium.com/nanonets/dense-and-sparse-crowd-counting-methods-and-techniques-a-review-bae04fdbf062 = dense crowd counting

outputs

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