# CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

arXiv: https://arxiv.org/abs/1608.06197 · HTML: https://mlsanity.com/paper/1608.06197/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 30 of 52)

- [Harnessing Perceptual Adversarial Patches for Crowd Counting](https://mlsanity.com/paper/2109.07986/) (2021)
- [Spatial Uncertainty-Aware Semi-Supervised Crowd Counting](https://mlsanity.com/paper/2107.13271/) (2021)
- [Cross-View Cross-Scene Multi-View Crowd Counting](https://mlsanity.com/paper/2205.01551/) (2022)
- [Instance and Panoptic Segmentation Using Conditional Convolutions](https://mlsanity.com/paper/2102.03026/) (2021)
- [Redesigning Multi-Scale Neural Network for Crowd Counting](https://mlsanity.com/paper/2208.02894/) (2022)
- [Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives](https://mlsanity.com/paper/2209.07271/) (2022)
- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting](https://mlsanity.com/paper/2202.08517/) (2022)
- [Spatio-channel Attention Blocks for Cross-modal Crowd Counting](https://mlsanity.com/paper/2210.10392/) (2022)
- [CountFormer: Multi-View Crowd Counting Transformer](https://mlsanity.com/paper/2407.02047/) (2024)
- [Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting](https://mlsanity.com/paper/2205.03307/) (2022)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [DeepTag: A General Framework for Fiducial Marker Design and Detection](https://mlsanity.com/paper/2105.13731/) (2021)
- [Multiscale Crowd Counting and Localization By Multitask Point Supervision](https://mlsanity.com/paper/2202.09942/) (2022)
- [Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation](https://mlsanity.com/paper/2206.10075/) (2022)
- [Multi-Camera Vehicle Counting Using Edge-AI](https://mlsanity.com/paper/2106.02842/) (2021)
- [A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches](https://mlsanity.com/paper/2501.19184/) (2025)
- [Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting](https://mlsanity.com/paper/2203.06388/) (2022)
- [LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance](https://mlsanity.com/paper/2302.05374/) (2023)
- [TreeFormer: a Semi-Supervised Transformer-based Framework for Tree Counting from a Single High Resolution Image](https://mlsanity.com/paper/2307.06118/) (2023)
- [Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting](https://mlsanity.com/paper/2201.04819/) (2022)
- [POLO -- Point-based, multi-class animal detection](https://mlsanity.com/paper/2410.11741/) (2024)
- [Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting](https://mlsanity.com/paper/2503.17984/) (2025)
- [Crowd counting with crowd attention convolutional neural network](https://mlsanity.com/paper/2204.07347/) (2022)
- [CLIP: Train Faster with Less Data](https://mlsanity.com/paper/2212.01452/) (2022)
- [Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs](https://mlsanity.com/paper/2310.07245/) (2023)
- [Hybrid attention network based on progressive embedding scale-context for crowd counting](https://mlsanity.com/paper/2106.02324/) (2021)
- [International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines](https://mlsanity.com/paper/2110.14613/) (2021)
- [Enhancing and Dissecting Crowd Counting By Synthetic Data](https://mlsanity.com/paper/2201.08992/) (2022)
- [DroneNet: Crowd Density Estimation using Self-ONNs for Drones](https://mlsanity.com/paper/2211.07137/) (2022)

## Abstract (arXiv)

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.