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


Counting Objects with Faster R-CNN

Drone-based Object Counting by Spatially Regularized Regional Proposal Network


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


Learning Short-Cut Connections for Object Counting

Object Counting with Small Datasets of Large Images


Counting with Focus for Free

Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting


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


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


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


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


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


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


Attention to Head Locations for Crowd Counting


Crowd Counting with Density Adaption Networks


Perspective-Aware CNN For Crowd Counting


Crowd Counting using Deep Recurrent Spatial-Aware Network

Top-Down Feedback for Crowd Counting Convolutional Neural Network


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


Attentive Crowd Flow Machines

Context-Aware Crowd Counting

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


Learning from Synthetic Data for Crowd Counting in the Wild

Point in, Box out: Beyond Counting Persons in Crowds

Crowd Transformer Network


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


PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

Dense Scale Network for Crowd Counting


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


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


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

Feature-aware Adaptation and Structured Density Alignment for Crowd Counting in Video Surveillance


AutoScale: Learning to Scale for Crowd Counting