CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

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.

Accepted at ACM Multimedia (MM) 2016

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by in corpus (9)

• A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
• Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
• Switching Convolutional Neural Network for Crowd Counting
• Crowd counting with crowd attention convolutional neural network
• CrossCount: A Deep Learning System for Device-free Human Counting using WiFi
• Crowd Counting Using Scale-Aware Attention Networks
• Crowd Transformer Network
• Counting dense objects in remote sensing images
• Per-Pixel Feedback for improving Semantic Segmentation