Object Counting

Published: 09 Oct 2015 Category: deep_learning

Object Counting

Towards perspective-free object counting with deep learning

Using Convolutional Neural Networks to Count Palm Trees in Satellite Images

Count-ception: Counting by Fully Convolutional Redundant Counting

https://arxiv.org/abs/1703.08710

Counting Objects with Faster R-CNN

Drone-based Object Counting by Spatially Regularized Regional Proposal Network

https://arxiv.org/abs/1707.05972

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

Representation Learning by Learning to Count

Leaf Counting with Deep Convolutional and Deconvolutional Networks

Improving Object Counting with Heatmap Regulation

https://arxiv.org/abs/1803.05494

Learning Short-Cut Connections for Object Counting

Object Counting with Small Datasets of Large Images

https://arxiv.org/abs/1805.11123

Counting with Focus for Free

Crowd Counting / Crowd Analysis

Large scale crowd analysis based on convolutional neural network

Deep People Counting in Extremely Dense Crowds

Crossing-line Crowd Counting with Two-phase Deep Neural Networks

Cross-scene Crowd Counting via Deep Convolutional Neural Networks

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

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

Crowd Counting by Adapting Convolutional Neural Networks with Side Information

Fully Convolutional Crowd Counting On Highly Congested Scenes

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

Multi-scale Convolutional Neural Networks for Crowd Counting

Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting

https://arxiv.org/abs/1703.09393

Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking

https://arxiv.org/abs/1705.10118

ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification

Image Crowd Counting Using Convolutional Neural Network and Markov Random Field

A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation

https://arxiv.org/abs/1707.01202

Spatiotemporal Modeling for Crowd Counting in Videos

CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting

Switching Convolutional Neural Network for Crowd Counting

Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs

Deep Spatial Regression Model for Image Crowd Counting

https://arxiv.org/abs/1710.09757

Crowd counting via scale-adaptive convolutional neural network

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

Structured Inhomogeneous Density Map Learning for Crowd Counting

https://arxiv.org/abs/1801.06642

Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

Crowd Counting via Adversarial Cross-Scale Consistency Pursuit

Crowd Counting with Deep Negative Correlation Learning

An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting

A Deeply-Recursive Convolutional Network for Crowd Counting

Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid

https://arxiv.org/abs/1805.06115

Attention to Head Locations for Crowd Counting

https://arxiv.org/abs/1806.10287

Crowd Counting with Density Adaption Networks

https://arxiv.org/abs/1806.10040

Perspective-Aware CNN For Crowd Counting

https://arxiv.org/abs/1807.01989

Crowd Counting using Deep Recurrent Spatial-Aware Network

Top-Down Feedback for Crowd Counting Convolutional Neural Network

https://arxiv.org/abs/1807.08881

Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance

In Defense of Single-column Networks for Crowd Counting

https://arxiv.org/abs/1808.06133

Attentive Crowd Flow Machines

Context-Aware Crowd Counting

ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding

https://arxiv.org/abs/1811.11968

Learning from Synthetic Data for Crowd Counting in the Wild

Point in, Box out: Beyond Counting Persons in Crowds

Crowd Transformer Network

https://arxiv.org/abs/1904.02774

DENet: A Universal Network for Counting Crowd with Varying Densities and Scales

https://arxiv.org/abs/1904.08056

PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

Dense Scale Network for Crowd Counting

https://arxiv.org/abs/1906.09707

Inverse Attention Guided Deep Crowd Counting Network

Locality-constrained Spatial Transformer Network for Video Crowd Counting

HA-CCN: Hierarchical Attention-based Crowd Counting Network

Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting

Deep Density-aware Count Regressor

https://arxiv.org/abs/1908.03314

Bayesian Loss for Crowd Count Estimation with Point Supervision

Crowd Counting with Deep Structured Scale Integration Network

Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

Awesome Crowd Counting

https://github.com/gjy3035/Awesome-Crowd-Counting

Learning Spatial Awareness to Improve Crowd Counting

  • intro: ICCV 2019 oral
  • intro: Southwest Jiaotong University & Carnegie Mellon University & Microsoft Research
  • keywords: SPatial Awareness Network (SPANet), Maximum Excess over Pixels (MEP) loss
  • arxiv: https://arxiv.org/abs/1909.07057

Perspective-Guided Convolution Networks for Crowd Counting

Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method