A simple Flask application that allow you to try different crowd counting models with different samples from various datasets
This project is based on the C^3-Framework and NPWU-Crowd-Sample-Code on which the models were trained:
- (C-3-Framework): Gao, Junyu and Lin, Wei and Zhao, Bin and Wang, Dong and Gao, Chenyu and Wen, Jun, C^3 Framework: An Open-source PyTorch Code for Crowd Counting, arXiv preprint arXiv:1907.02724, (2019)
This repo also contains some samples from various dataset:
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(ShanghaiTech): Yingying Zhang, Desen Zhou, Single Image Crowd Counting via Multi Column Convolutional Neural Network, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
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(NPWU-CROWD): Wang, Qi and Gao, Junyu and Lin, Wei and Li, Xuelong, NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2020.
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(UCC-CF-50): Haroon Idrees, Imran Saleemi, Cody Seibert, Mubarak Shah, Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
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(QNRF): H. Idrees, M. Tayyab, K. Athrey, D. Zhang, S. Al-Maddeed, N. Rajpoot, M. Shah, Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds, in Proceedings of IEEE European Conference on Computer Vision (ECCV 2018), Munich, Germany, September 8-14, 2018.