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Crowd Counting

39 papers with code · Computer Vision
Subtask of Crowds

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Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

CROWD COUNTING DOMAIN GENERALIZATION IMAGE CLASSIFICATION OBJECT DETECTION SEMANTIC SEGMENTATION

Densely Connected Convolutional Networks

CVPR 2017 pytorch/vision

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

CROWD COUNTING IMAGE CLASSIFICATION OBJECT RECOGNITION

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2 Nov 2015divamgupta/image-segmentation-keras

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

CROWD COUNTING LESION SEGMENTATION REAL-TIME SEMANTIC SEGMENTATION SCENE SEGMENTATION SCENE UNDERSTANDING

CNN-based Density Estimation and Crowd Counting: A Survey

28 Mar 2020gjy3035/Awesome-Crowd-Counting

Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.

CROWD COUNTING DENSITY ESTIMATION OBJECT COUNTING

NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting

10 Jan 2020gjy3035/Awesome-Crowd-Counting

In the last decade, crowd counting attracts much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.

CROWD COUNTING

C^3 Framework: An Open-source PyTorch Code for Crowd Counting

5 Jul 2019gjy3035/C-3-Framework

This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).

CROWD COUNTING

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

CVPR 2018 leeyeehoo/CSRNet-pytorch

We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.

CROWD COUNTING SCENE RECOGNITION

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

Conference 2016 svishwa/crowdcount-mcnn

To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map.

CROWD COUNTING

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

17 Feb 2019xialeiliu/RankIQA

Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.

ACTIVE LEARNING CROWD COUNTING IMAGE QUALITY ASSESSMENT LEARNING-TO-RANK SELF-SUPERVISED LEARNING

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

22 Aug 2016davideverona/deep-crowd-counting_crowdnet

Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds.

CROWD COUNTING DATA AUGMENTATION