CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

TL;DR

This paper introduces CrowdNet, a deep convolutional network that combines deep and shallow features to accurately estimate crowd density from images, effectively handling scale variations and limited training data.

Contribution

CrowdNet is a novel deep learning framework that integrates multi-scale data augmentation and combined feature extraction for dense crowd counting.

Findings

Outperforms state-of-the-art methods on UCF_CC_50 dataset

Effectively captures both high-level and low-level features for crowd density estimation

Uses multi-scale data augmentation to handle limited training data

Abstract

Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image. Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under large scale variations. As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multi-scale data augmentation. Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations. Our method is tested on the challenging UCF_CC_50 dataset, and shown to outperform the state of the art methods.