# Image Crowd Counting Using Convolutional Neural Network and Markov Random Field

## TL;DR

This paper introduces a CNN-MRF approach for crowd counting in images, combining deep learning feature extraction with Markov random fields to improve accuracy over existing methods.

## Contribution

The paper presents a novel combination of CNN and MRF for crowd counting, effectively leveraging patch correlations to enhance counting accuracy.

## Key findings

- Outperforms state-of-the-art methods on UCF dataset
- Achieves significant improvements on Shanghaitech dataset
- Demonstrates the effectiveness of combining CNN with MRF

## Abstract

In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the local patches. Experiments show that our approach significantly outperforms the state-of-the-art methods on UCF and Shanghaitech crowd counting datasets.