• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号

taohan10200/Awesome-Crowd-Localization: Awesome Crowd Localization

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

taohan10200/Awesome-Crowd-Localization

开源软件地址(OpenSource Url):

https://github.com/taohan10200/Awesome-Crowd-Localization

开源编程语言(OpenSource Language):


开源软件介绍(OpenSource Introduction):

Awesome-Crowd-Localization

Awesome Crowd Localization

Contents

Misc

Relaetd Tasks

Challenge

  • NWPU-Crowd Localization: Link
  • The 1st Tiny Object Detection Challenge: Link

Metrics

  • mAP, mAR in RAZNet (namely key point evaluation in COCO: fixed sigma)
  • F1-m, Precision, Recall in NWPU-Crowd (scale-aware sigma)
  • MLE in LSC-CNN (distance measure)

Datasets

  • NWPU-Crowd (dot, box)
  • JHU-CROWD (dot, size)
  • FDST (dot, box)
  • Head Tracking 21 (dot, box, id) [Download]

Papers

Arxiv

  • [DCST] Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer [paper]
  • [GNA] Video Crowd Localization with Multi-focus Gaussian Neighbor Attention and a Large-Scale Benchmark [paper]
  • [SCALNet] Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization [paper] [code]
  • [FIDTM] Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd [paper] [code]
  • [RDTM] Reciprocal Distance Transform Maps for Crowd Counting and People Localization in Dense Crowd [paper] [code]
  • Counting and Locating High-Density Objects Using Convolutional Neural Network [paper]
  • [IIM] Learning Independent Instance Maps for Crowd Localization [paper] [code]
  • [AutoScale] Autoscale: learning to scale for crowd counting [paper] [code]
  • A Strong Baseline for Crowd Counting and Unsupervised People Localization [paper]
  • Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network [paper][code]

2021

  • [SA-InterNet] A-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization (PRVC) [paper]
  • A smartly simple way for joint crowd counting and localization (Neurocomputing) [aper]
  • A Generalized Loss Function for Crowd Counting and Localization (CVPR) [paper]
  • [ P2PNet] Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework (ICCV) [paper]
  • [D2CNet] Decoupled Two-Stage Crowd Counting and Beyond (TIP) [paper] [code]
  • [Crowd-SDNet] A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds (TIP) [paper] [code]
  • [TopoCount] Localization in the Crowd with Topological Constraints (AAAI2021) [paper][code]

2020

  • [DD-CNN] Going Beyond the Regression Paradigm with Accurate Dot Prediction for Dense Crowds (WACV) [paper]
  • [NWPU] NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization (T-PAMI) [paper][code]
  • [LSC-CNN] Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection (T-PAMI) [paper][code]
  • Scale Match for Tiny Person Detection (WACV) [paper][code]

2019

  • Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (CVPR) [paper]
  • Point in, Box out: Beyond Counting Persons in Crowds (CVPR) [paper]
  • [RAZ_Loc] Recurrent attentive zooming for joint crowd counting and precise localization (CVPR) [paper] [Reproduction_code]
  • [RDNet] Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (CVPR) [paper][code]

2018

  • [CL] Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (ECCV) [paper]
  • [LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV) [paper] [code]
  • SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network (ECCV) [paper]

2017

  • Focal Loss for Dense Object Detection (ICCV) [paper]
  • [TinyFaces] Finding tiny faces (CVPR) [paper]
  • Perceptual Generative Adversarial Networks for Small Object Detection (CVPR) [paper]

2015

  • Small Instance Detection by Integer Programming on Object Density Maps, (CVPR) [paper ]
  • End-to-end people detection in crowded scenes (CVPR) [paper] [code]
  • [Faster-RCNN] Towards real-time object detection with region proposal networks (CVPR) [paper] [code]

Leaderboard

NWPU

More detailed results are in this link.

Year--Conference/Journal Methods Backbone F1-measure Precise Recall A0~A5 Avg.
2015--NIPS Faster RCNN ResNet-101 6.7 95.8 3.5 0/0.002/0.4/7.9/37.2/63.5 18.2
2017--CVPR TinyFaces ResNet-101 56.7 52.9 61.1 4.2/22.6/59.1/90.0/93.1/89.6 59.8
2019--arXiv VGG+GPR VGG-16 52.5 55.8 49.6 3.1/27.2/49.1/68.7/49.8/26.3 37.4
2019--CVPR RAZ_Loc VGG-16 59.8 66.6 54.3 3.1/27.2/49.1/68.7/49.8/26.3 42.4
2021--TIP Crowd-SDNet ResNet-50 63.7 65.1 62.4 7.3/43.7/62.4/75.7/71.2/70.2 55.1
2021--AAAI TopoCount VGG-16 69.2 68.3 70.1 5.7/39.1/72.2/85.7/87.3/89.7 63.3
2021--arXiv RDTM VGG-16 69.9 75.1 65.4 11.5/46.3/68.5/74.9/54.6/18.2 45.7
2021--arXiv SCALNet DLA-34 69.1 69.2 69.0 - -
2021--TIP D2CNet VGG-16 70.0 74.1 66.2 11.3/50.2/67.8/74.5/69.5/76.5 58.3
2020--arXiv IIM VGG-16 73.2 77.9 69.2 10.1/44.1/70.7/82.4/83.0/61.4 58.7
2020--arXiv IIM HRNet 76.2 81.3 71.7 12.0/46.0/73.2/85.5/86.7/64.3 61.3



鲜花

握手

雷人

路过

鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
热门推荐
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服(服务时间 9:00~18:00)

在线QQ客服
地址:深圳市南山区西丽大学城创智工业园
电邮:jeky_zhao#qq.com
移动电话:139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap